{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f6ce2ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import Data_Align\n",
    "import importlib\n",
    "importlib.reload(Data_Align)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# import typing\n",
    "# from numpy.lib.histograms import _hist_bin_sqrt\n",
    "# from numpy.polynomial import Polynomial\n",
    "# from pprint import pprint\n",
    "# import pypg\n",
    "# from sklearn.metrics import pairwise_distances\n",
    "# from sklearn.cluster import AgglomerativeClustering\n",
    "# import scipy\n",
    "# from scipy.linalg import hankel\n",
    "# from scipy.optimize import minimize\n",
    "# from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import signal\n",
    "# import padasip as pa\n",
    "import nolds\n",
    "# import antropy\n",
    "# import entropy\n",
    "# import EntropyHub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "505cb134",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-30 10:57:08,193 - INFO - ===== 开始处理文件: /Users/yukunlong/Documents/working/ppg_modeling/test4Pang/gateway_eegdata_csv/庞日宠_1752046302_1752047814_precedent_stage_signals_1.csv =====\n",
      "2025-07-30 10:57:08,949 - INFO - 成功加载数据，共 754000 行，列名：['timestep', 'device_info_id', 'eeg_channel1', 'eeg_channel2', 'eeg_channel3', 'eeg_channel4', 'eog_channel1', 'eog_channel2', 'trigger_channel', 'ppg_red', 'ppg_infrared', 'imu_acc_x', 'imu_acc_y', 'imu_acc_z', 'imu_gyro_x', 'imu_gyro_y', 'imu_gyro_z', 'image_path', 'label']\n",
      "2025-07-30 10:57:09,222 - INFO - 检测到起始trigger: 1，时间戳: 1752046338908.0ms\n",
      "2025-07-30 10:57:09,707 - INFO - 检测到结束trigger: 10，时间戳: 1752046458960.0ms\n",
      "2025-07-30 10:57:09,707 - INFO - 事件匹配成功: 1 -> 10，时间范围: 1752046338908.0ms ~ 1752046458960.0ms，持续 120052.0ms\n",
      "2025-07-30 10:57:09,754 - INFO - 检测到起始trigger: 6，时间戳: 1752046470292.0ms\n",
      "2025-07-30 10:57:10,482 - INFO - 检测到结束trigger: 60，时间戳: 1752046650370.0ms\n",
      "2025-07-30 10:57:10,482 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752046470292.0ms ~ 1752046650370.0ms，持续 180078.0ms\n",
      "2025-07-30 10:57:10,536 - INFO - 检测到起始trigger: 4，时间戳: 1752046661590.0ms\n",
      "2025-07-30 10:57:11,021 - INFO - 检测到结束trigger: 40，时间戳: 1752046781644.0ms\n",
      "2025-07-30 10:57:11,021 - INFO - 事件匹配成功: 4 -> 40，时间范围: 1752046661590.0ms ~ 1752046781644.0ms，持续 120054.0ms\n",
      "2025-07-30 10:57:11,065 - INFO - 检测到起始trigger: 6，时间戳: 1752046792492.0ms\n",
      "2025-07-30 10:57:11,791 - INFO - 检测到结束trigger: 60，时间戳: 1752046972544.0ms\n",
      "2025-07-30 10:57:11,791 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752046792492.0ms ~ 1752046972544.0ms，持续 180052.0ms\n",
      "2025-07-30 10:57:11,831 - INFO - 检测到起始trigger: 23，时间戳: 1752046982292.0ms\n",
      "2025-07-30 10:57:11,959 - INFO - 检测到结束trigger: 20，时间戳: 1752047013910.0ms\n",
      "2025-07-30 10:57:11,960 - INFO - 事件匹配成功: 23 -> 20，时间范围: 1752046982292.0ms ~ 1752047013910.0ms，持续 31618.0ms\n",
      "2025-07-30 10:57:11,993 - INFO - 检测到起始trigger: 24，时间戳: 1752047022010.0ms\n",
      "2025-07-30 10:57:12,121 - INFO - 检测到结束trigger: 20，时间戳: 1752047053100.0ms\n",
      "2025-07-30 10:57:12,121 - INFO - 事件匹配成功: 24 -> 20，时间范围: 1752047022010.0ms ~ 1752047053100.0ms，持续 31090.0ms\n",
      "2025-07-30 10:57:12,155 - INFO - 检测到起始trigger: 21，时间戳: 1752047061394.0ms\n",
      "2025-07-30 10:57:12,272 - INFO - 检测到结束trigger: 20，时间戳: 1752047089730.0ms\n",
      "2025-07-30 10:57:12,272 - INFO - 事件匹配成功: 21 -> 20，时间范围: 1752047061394.0ms ~ 1752047089730.0ms，持续 28336.0ms\n",
      "2025-07-30 10:57:12,305 - INFO - 检测到起始trigger: 22，时间戳: 1752047097610.0ms\n",
      "2025-07-30 10:57:12,461 - INFO - 检测到结束trigger: 20，时间戳: 1752047135458.0ms\n",
      "2025-07-30 10:57:12,461 - INFO - 事件匹配成功: 22 -> 20，时间范围: 1752047097610.0ms ~ 1752047135458.0ms，持续 37848.0ms\n",
      "2025-07-30 10:57:12,509 - INFO - 检测到起始trigger: 6，时间戳: 1752047147142.0ms\n",
      "2025-07-30 10:57:13,251 - INFO - 检测到结束trigger: 60，时间戳: 1752047327194.0ms\n",
      "2025-07-30 10:57:13,251 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752047147142.0ms ~ 1752047327194.0ms，持续 180052.0ms\n",
      "2025-07-30 10:57:13,296 - INFO - 检测到起始trigger: 3，时间戳: 1752047337926.0ms\n",
      "2025-07-30 10:57:13,792 - INFO - 检测到结束trigger: 30，时间戳: 1752047457956.0ms\n",
      "2025-07-30 10:57:13,792 - INFO - 事件匹配成功: 3 -> 30，时间范围: 1752047337926.0ms ~ 1752047457956.0ms，持续 120030.0ms\n",
      "2025-07-30 10:57:13,862 - INFO - 检测到起始trigger: 6，时间戳: 1752047474876.0ms\n",
      "2025-07-30 10:57:14,592 - INFO - 检测到结束trigger: 60，时间戳: 1752047654928.0ms\n",
      "2025-07-30 10:57:14,593 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752047474876.0ms ~ 1752047654928.0ms，持续 180052.0ms\n",
      "2025-07-30 10:57:14,703 - INFO - 检测到起始trigger: 5，时间戳: 1752047681734.0ms\n",
      "2025-07-30 10:57:15,190 - INFO - 检测到结束trigger: 50，时间戳: 1752047801796.0ms\n",
      "2025-07-30 10:57:15,191 - INFO - 事件匹配成功: 5 -> 50，时间范围: 1752047681734.0ms ~ 1752047801796.0ms，持续 120062.0ms\n",
      "2025-07-30 10:57:15,240 - INFO - 文件处理完成，共检测到 12 个有效事件\n",
      "2025-07-30 10:57:15,241 - INFO - ===== 处理结束 =====\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "成功读取文件: aligned_17.csv，数据形状: (105736, 5)\n",
      "成功读取文件: aligned_16.csv，数据形状: (359898, 5)\n",
      "成功读取文件: aligned_15.csv，数据形状: (212407, 5)\n",
      "所有文件合并完成，总数据形状: (678041, 5)\n",
      "成功读取文件: aligned_17.csv，数据形状: (105874, 7)\n",
      "成功读取文件: aligned_16.csv，数据形状: (359796, 7)\n",
      "成功读取文件: aligned_15.csv，数据形状: (213000, 7)\n",
      "所有文件合并完成，总数据形状: (678670, 7)\n",
      "成功读取文件: 16.csv，数据形状: (359, 2)\n",
      "成功读取文件: 17.csv，数据形状: (105, 2)\n",
      "成功读取文件: 15.csv，数据形状: (210, 2)\n",
      "所有文件合并完成，总数据形状: (674, 2)\n",
      "成功读取文件: aligned_17.csv，数据形状: (1084, 2)\n",
      "成功读取文件: aligned_16.csv，数据形状: (4233, 2)\n",
      "成功读取文件: aligned_15.csv，数据形状: (2362, 2)\n",
      "所有文件合并完成，总数据形状: (7679, 2)\n",
      "帧率抖动: 8960.04 ms\n",
      "丢包率: 0.0\n",
      "真实采样率: -188.4\n",
      "对齐后脑eeg数据：(754000, 19)\n",
      "对齐后脑ppg数据：(150800, 5)\n",
      "对齐后脑电采集时间：1508.0\n",
      "对齐后PPG的采集时间：1508.0\n",
      "ppg开始时间1752046338910.0ms, imu开始时间1752046338910.0ms\n",
      "ppg开始时间1752046470300.0ms, imu开始时间1752046470300.0ms\n",
      "ppg开始时间1752046661590.0ms, imu开始时间1752046661590.0ms\n",
      "ppg开始时间1752046792500.0ms, imu开始时间1752046792500.0ms\n",
      "ppg开始时间1752046982300.0ms, imu开始时间1752046982300.0ms\n",
      "ppg开始时间1752047022010.0ms, imu开始时间1752047022010.0ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-30 10:57:16,825 - INFO - ===== 开始处理文件: /Users/yukunlong/Documents/working/ppg_modeling/test4Pang/gateway_eegdata_csv/庞日宠_1752050945_1752052418_posterior_stage_signals_1.csv =====\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ppg开始时间1752047061400.0ms, imu开始时间1752047061400.0ms\n",
      "ppg开始时间1752047097610.0ms, imu开始时间1752047097610.0ms\n",
      "ppg开始时间1752047147150.0ms, imu开始时间1752047147150.0ms\n",
      "ppg开始时间1752047337930.0ms, imu开始时间1752047337930.0ms\n",
      "ppg开始时间1752047474880.0ms, imu开始时间1752047474880.0ms\n",
      "ppg开始时间1752047681740.0ms, imu开始时间1752047681740.0ms\n",
      "\n",
      "事件1：\n",
      "  触发器: 1->10\n",
      "  时间范围: 1752046338908.0ms - 1752046458960.0ms\n",
      "  提取到EEG样本数: 60027\n",
      "  提取到PPG样本数: 12006\n",
      "  提取到IMU样本数: 12006\n",
      "\n",
      "事件2：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752046470292.0ms - 1752046650370.0ms\n",
      "  提取到EEG样本数: 90040\n",
      "  提取到PPG样本数: 18008\n",
      "  提取到IMU样本数: 18008\n",
      "\n",
      "事件3：\n",
      "  触发器: 4->40\n",
      "  时间范围: 1752046661590.0ms - 1752046781644.0ms\n",
      "  提取到EEG样本数: 60028\n",
      "  提取到PPG样本数: 12006\n",
      "  提取到IMU样本数: 12006\n",
      "\n",
      "事件4：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752046792492.0ms - 1752046972544.0ms\n",
      "  提取到EEG样本数: 90027\n",
      "  提取到PPG样本数: 18005\n",
      "  提取到IMU样本数: 18005\n",
      "\n",
      "事件5：\n",
      "  触发器: 23->20\n",
      "  时间范围: 1752046982292.0ms - 1752047013910.0ms\n",
      "  提取到EEG样本数: 15810\n",
      "  提取到PPG样本数: 3162\n",
      "  提取到IMU样本数: 3162\n",
      "\n",
      "事件6：\n",
      "  触发器: 24->20\n",
      "  时间范围: 1752047022010.0ms - 1752047053100.0ms\n",
      "  提取到EEG样本数: 15546\n",
      "  提取到PPG样本数: 3110\n",
      "  提取到IMU样本数: 3110\n",
      "\n",
      "事件7：\n",
      "  触发器: 21->20\n",
      "  时间范围: 1752047061394.0ms - 1752047089730.0ms\n",
      "  提取到EEG样本数: 14169\n",
      "  提取到PPG样本数: 2834\n",
      "  提取到IMU样本数: 2834\n",
      "\n",
      "事件8：\n",
      "  触发器: 22->20\n",
      "  时间范围: 1752047097610.0ms - 1752047135458.0ms\n",
      "  提取到EEG样本数: 18925\n",
      "  提取到PPG样本数: 3785\n",
      "  提取到IMU样本数: 3785\n",
      "\n",
      "事件9：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752047147142.0ms - 1752047327194.0ms\n",
      "  提取到EEG样本数: 90027\n",
      "  提取到PPG样本数: 18005\n",
      "  提取到IMU样本数: 18005\n",
      "\n",
      "事件10：\n",
      "  触发器: 3->30\n",
      "  时间范围: 1752047337926.0ms - 1752047457956.0ms\n",
      "  提取到EEG样本数: 60016\n",
      "  提取到PPG样本数: 12003\n",
      "  提取到IMU样本数: 12003\n",
      "\n",
      "事件11：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752047474876.0ms - 1752047654928.0ms\n",
      "  提取到EEG样本数: 90027\n",
      "  提取到PPG样本数: 18005\n",
      "  提取到IMU样本数: 18005\n",
      "\n",
      "事件12：\n",
      "  触发器: 5->50\n",
      "  时间范围: 1752047681734.0ms - 1752047801796.0ms\n",
      "  提取到EEG样本数: 60032\n",
      "  提取到PPG样本数: 12006\n",
      "  提取到IMU样本数: 12006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-07-30 10:57:17,554 - INFO - 成功加载数据，共 736000 行，列名：['timestep', 'device_info_id', 'eeg_channel1', 'eeg_channel2', 'eeg_channel3', 'eeg_channel4', 'eog_channel1', 'eog_channel2', 'trigger_channel', 'ppg_red', 'ppg_infrared', 'imu_acc_x', 'imu_acc_y', 'imu_acc_z', 'imu_gyro_x', 'imu_gyro_y', 'imu_gyro_z', 'image_path', 'label']\n",
      "2025-07-30 10:57:17,762 - INFO - 检测到起始trigger: 5，时间戳: 1752050965500.0ms\n",
      "2025-07-30 10:57:18,256 - INFO - 检测到结束trigger: 50，时间戳: 1752051085564.0ms\n",
      "2025-07-30 10:57:18,256 - INFO - 事件匹配成功: 5 -> 50，时间范围: 1752050965500.0ms ~ 1752051085564.0ms，持续 120064.0ms\n",
      "2025-07-30 10:57:18,340 - INFO - 检测到起始trigger: 6，时间戳: 1752051106012.0ms\n",
      "2025-07-30 10:57:19,075 - INFO - 检测到结束trigger: 60，时间戳: 1752051286064.0ms\n",
      "2025-07-30 10:57:19,075 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752051106012.0ms ~ 1752051286064.0ms，持续 180052.0ms\n",
      "2025-07-30 10:57:19,099 - INFO - 检测到起始trigger: 1，时间戳: 1752051291712.0ms\n",
      "2025-07-30 10:57:19,594 - INFO - 检测到结束trigger: 10，时间戳: 1752051411764.0ms\n",
      "2025-07-30 10:57:19,595 - INFO - 事件匹配成功: 1 -> 10，时间范围: 1752051291712.0ms ~ 1752051411764.0ms，持续 120052.0ms\n",
      "2025-07-30 10:57:19,626 - INFO - 检测到起始trigger: 6，时间戳: 1752051419428.0ms\n",
      "2025-07-30 10:57:20,370 - INFO - 检测到结束trigger: 60，时间戳: 1752051599480.0ms\n",
      "2025-07-30 10:57:20,370 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752051419428.0ms ~ 1752051599480.0ms，持续 180052.0ms\n",
      "2025-07-30 10:57:20,408 - INFO - 检测到起始trigger: 25，时间戳: 1752051608646.0ms\n",
      "2025-07-30 10:57:20,512 - INFO - 检测到结束trigger: 20，时间戳: 1752051633986.0ms\n",
      "2025-07-30 10:57:20,513 - INFO - 事件匹配成功: 25 -> 20，时间范围: 1752051608646.0ms ~ 1752051633986.0ms，持续 25340.0ms\n",
      "2025-07-30 10:57:20,548 - INFO - 检测到起始trigger: 28，时间戳: 1752051642496.0ms\n",
      "2025-07-30 10:57:20,673 - INFO - 检测到结束trigger: 20，时间戳: 1752051672986.0ms\n",
      "2025-07-30 10:57:20,674 - INFO - 事件匹配成功: 28 -> 20，时间范围: 1752051642496.0ms ~ 1752051672986.0ms，持续 30490.0ms\n",
      "2025-07-30 10:57:20,723 - INFO - 检测到起始trigger: 27，时间戳: 1752051682562.0ms\n",
      "2025-07-30 10:57:20,829 - INFO - 检测到结束trigger: 20，时间戳: 1752051708272.0ms\n",
      "2025-07-30 10:57:20,829 - INFO - 事件匹配成功: 27 -> 20，时间范围: 1752051682562.0ms ~ 1752051708272.0ms，持续 25710.0ms\n",
      "2025-07-30 10:57:20,867 - INFO - 检测到起始trigger: 26，时间戳: 1752051717346.0ms\n",
      "2025-07-30 10:57:20,978 - INFO - 检测到结束trigger: 20，时间戳: 1752051744092.0ms\n",
      "2025-07-30 10:57:20,978 - INFO - 事件匹配成功: 26 -> 20，时间范围: 1752051717346.0ms ~ 1752051744092.0ms，持续 26746.0ms\n",
      "2025-07-30 10:57:21,058 - INFO - 检测到起始trigger: 6，时间戳: 1752051763130.0ms\n",
      "2025-07-30 10:57:21,802 - INFO - 检测到结束trigger: 60，时间戳: 1752051943182.0ms\n",
      "2025-07-30 10:57:21,802 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752051763130.0ms ~ 1752051943182.0ms，持续 180052.0ms\n",
      "2025-07-30 10:57:21,828 - INFO - 检测到起始trigger: 4，时间戳: 1752051949460.0ms\n",
      "2025-07-30 10:57:22,320 - INFO - 检测到结束trigger: 40，时间戳: 1752052069514.0ms\n",
      "2025-07-30 10:57:22,320 - INFO - 事件匹配成功: 4 -> 40，时间范围: 1752051949460.0ms ~ 1752052069514.0ms，持续 120054.0ms\n",
      "2025-07-30 10:57:22,355 - INFO - 检测到起始trigger: 6，时间戳: 1752052077930.0ms\n",
      "2025-07-30 10:57:23,092 - INFO - 检测到结束trigger: 60，时间戳: 1752052257980.0ms\n",
      "2025-07-30 10:57:23,092 - INFO - 事件匹配成功: 6 -> 60，时间范围: 1752052077930.0ms ~ 1752052257980.0ms，持续 180050.0ms\n",
      "2025-07-30 10:57:23,202 - INFO - 检测到起始trigger: 3，时间戳: 1752052284468.0ms\n",
      "2025-07-30 10:57:23,690 - INFO - 检测到结束trigger: 30，时间戳: 1752052404470.0ms\n",
      "2025-07-30 10:57:23,691 - INFO - 事件匹配成功: 3 -> 30，时间范围: 1752052284468.0ms ~ 1752052404470.0ms，持续 120002.0ms\n",
      "2025-07-30 10:57:23,750 - INFO - 文件处理完成，共检测到 12 个有效事件\n",
      "2025-07-30 10:57:23,751 - INFO - ===== 处理结束 =====\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "成功读取文件: aligned_17.csv，数据形状: (105736, 5)\n",
      "成功读取文件: aligned_16.csv，数据形状: (359898, 5)\n",
      "成功读取文件: aligned_15.csv，数据形状: (212407, 5)\n",
      "所有文件合并完成，总数据形状: (678041, 5)\n",
      "成功读取文件: aligned_17.csv，数据形状: (105874, 7)\n",
      "成功读取文件: aligned_16.csv，数据形状: (359796, 7)\n",
      "成功读取文件: aligned_15.csv，数据形状: (213000, 7)\n",
      "所有文件合并完成，总数据形状: (678670, 7)\n",
      "成功读取文件: 16.csv，数据形状: (359, 2)\n",
      "成功读取文件: 17.csv，数据形状: (105, 2)\n",
      "成功读取文件: 15.csv，数据形状: (210, 2)\n",
      "所有文件合并完成，总数据形状: (674, 2)\n",
      "成功读取文件: aligned_17.csv，数据形状: (1084, 2)\n",
      "成功读取文件: aligned_16.csv，数据形状: (4233, 2)\n",
      "成功读取文件: aligned_15.csv，数据形状: (2362, 2)\n",
      "所有文件合并完成，总数据形状: (7679, 2)\n",
      "帧率抖动: 8960.04 ms\n",
      "丢包率: 0.0\n",
      "真实采样率: -188.4\n",
      "对齐后脑eeg数据：(736000, 19)\n",
      "对齐后脑ppg数据：(147200, 5)\n",
      "对齐后脑电采集时间：1472.0\n",
      "对齐后PPG的采集时间：1472.0\n",
      "ppg开始时间1752050965508.0ms, imu开始时间1752050965510.0ms\n",
      "ppg开始时间1752051106018.0ms, imu开始时间1752051106020.0ms\n",
      "ppg开始时间1752051291718.0ms, imu开始时间1752051291720.0ms\n",
      "ppg开始时间1752051419428.0ms, imu开始时间1752051419430.0ms\n",
      "ppg开始时间1752051608655.0ms, imu开始时间1752051608650.0ms\n",
      "ppg开始时间1752051642505.0ms, imu开始时间1752051642500.0ms\n",
      "ppg开始时间1752051682565.0ms, imu开始时间1752051682560.0ms\n",
      "ppg开始时间1752051717355.0ms, imu开始时间1752051717350.0ms\n",
      "ppg开始时间1752051763135.0ms, imu开始时间1752051763130.0ms\n",
      "ppg开始时间1752051949465.0ms, imu开始时间1752051949460.0ms\n",
      "ppg开始时间1752052077935.0ms, imu开始时间1752052077930.0ms\n",
      "ppg开始时间1752052284475.0ms, imu开始时间1752052284470.0ms\n",
      "\n",
      "事件1：\n",
      "  触发器: 5->50\n",
      "  时间范围: 1752050965500.0ms - 1752051085564.0ms\n",
      "  提取到EEG样本数: 60033\n",
      "  提取到PPG样本数: 12006\n",
      "  提取到IMU样本数: 12006\n",
      "\n",
      "事件2：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752051106012.0ms - 1752051286064.0ms\n",
      "  提取到EEG样本数: 90027\n",
      "  提取到PPG样本数: 18005\n",
      "  提取到IMU样本数: 18005\n",
      "\n",
      "事件3：\n",
      "  触发器: 1->10\n",
      "  时间范围: 1752051291712.0ms - 1752051411764.0ms\n",
      "  提取到EEG样本数: 60027\n",
      "  提取到PPG样本数: 12005\n",
      "  提取到IMU样本数: 12005\n",
      "\n",
      "事件4：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752051419428.0ms - 1752051599480.0ms\n",
      "  提取到EEG样本数: 90027\n",
      "  提取到PPG样本数: 18006\n",
      "  提取到IMU样本数: 18006\n",
      "\n",
      "事件5：\n",
      "  触发器: 25->20\n",
      "  时间范围: 1752051608646.0ms - 1752051633986.0ms\n",
      "  提取到EEG样本数: 12671\n",
      "  提取到PPG样本数: 2534\n",
      "  提取到IMU样本数: 2534\n",
      "\n",
      "事件6：\n",
      "  触发器: 28->20\n",
      "  时间范围: 1752051642496.0ms - 1752051672986.0ms\n",
      "  提取到EEG样本数: 15246\n",
      "  提取到PPG样本数: 3049\n",
      "  提取到IMU样本数: 3049\n",
      "\n",
      "事件7：\n",
      "  触发器: 27->20\n",
      "  时间范围: 1752051682562.0ms - 1752051708272.0ms\n",
      "  提取到EEG样本数: 12856\n",
      "  提取到PPG样本数: 2571\n",
      "  提取到IMU样本数: 2571\n",
      "\n",
      "事件8：\n",
      "  触发器: 26->20\n",
      "  时间范围: 1752051717346.0ms - 1752051744092.0ms\n",
      "  提取到EEG样本数: 13374\n",
      "  提取到PPG样本数: 2674\n",
      "  提取到IMU样本数: 2674\n",
      "\n",
      "事件9：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752051763130.0ms - 1752051943182.0ms\n",
      "  提取到EEG样本数: 90027\n",
      "  提取到PPG样本数: 18005\n",
      "  提取到IMU样本数: 18005\n",
      "\n",
      "事件10：\n",
      "  触发器: 4->40\n",
      "  时间范围: 1752051949460.0ms - 1752052069514.0ms\n",
      "  提取到EEG样本数: 60028\n",
      "  提取到PPG样本数: 12005\n",
      "  提取到IMU样本数: 12005\n",
      "\n",
      "事件11：\n",
      "  触发器: 6->60\n",
      "  时间范围: 1752052077930.0ms - 1752052257980.0ms\n",
      "  提取到EEG样本数: 90026\n",
      "  提取到PPG样本数: 18005\n",
      "  提取到IMU样本数: 18005\n",
      "\n",
      "事件12：\n",
      "  触发器: 3->30\n",
      "  时间范围: 1752052284468.0ms - 1752052404470.0ms\n",
      "  提取到EEG样本数: 60002\n",
      "  提取到PPG样本数: 12000\n",
      "  提取到IMU样本数: 12000\n"
     ]
    }
   ],
   "source": [
    "# 读取前测eeg ppg imu\n",
    "precedent_eeg_path=r\"/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/gateway_eegdata_csv/庞日宠_1752046302_1752047814_precedent_stage_signals_1.csv\"\n",
    "ppg_folder=r\"/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/pang_watch_data/ppg_green_raw/250709/\"\n",
    "imu_folder=r\"/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/pang_watch_data/imu_raw/250709\"\n",
    "hr_folder=r\"/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/pang_watch_data/ppg_hr/250709\"\n",
    "hrv_folder=r\"/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/pang_watch_data/ppg_hrv/250709\"\n",
    "pre_event_signals=Data_Align.align_Epoch_data(precedent_eeg_path, ppg_folder, imu_folder, hr_folder, hrv_folder)\n",
    "# 读取后测eeg ppg imu\n",
    "post_eeg_path=r\"/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/gateway_eegdata_csv/庞日宠_1752050945_1752052418_posterior_stage_signals_1.csv\"\n",
    "post_event_signals=Data_Align.align_Epoch_data(post_eeg_path, ppg_folder, imu_folder, hr_folder, hrv_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6862c191",
   "metadata": {},
   "outputs": [],
   "source": [
    "# class ssa_process:    \n",
    "#     def __init__(self, n_freq_bins=4096):\n",
    "#         self.n_freq_bins = n_freq_bins\n",
    "        \n",
    "#     def ssa(self,ts: np.ndarray, L: int, perform_grouping: bool = True, wcorr_threshold: float = 0.3, ret_Wcorr: bool = False):\n",
    "#             \"\"\"\n",
    "#             Performs SSA on ts\n",
    "#             https://www.kaggle.com/jdarcy/introducing-ssa-for-time-series-decomposition\n",
    "\n",
    "#             Parameters\n",
    "#             ----------\n",
    "#                 ts : ndarray of shape (n_timestamps, )\n",
    "#                     The time series to decompose\n",
    "#                 L : int\n",
    "#                     first dimension of the L-trajectory-matrix\n",
    "#                 grouping : bool, default=True\n",
    "#                     If True, perform grouping based on the w-correlations of the deconstructed time series\n",
    "#                     using agglomerative hierarchical clustering with single linkage.\n",
    "#                     If this parameter is True, the parameter distance_threshold must be set.\n",
    "#                 wcorr_threshold : float, default=0.3\n",
    "#                     The w-correlation threshold used with the agglomerative hierarchical clustering.\n",
    "#                     Time series with at least this w-correlation will be grouped together.\n",
    "#                     This parameter will be ignored if grouping is set to False.\n",
    "#                 ret_Wcorr : bool, default=False\n",
    "#                     Whether the resulting w-correlation matrix should be returned.\n",
    "#                     If grouping is enabled, return the w-correlation matrix of the grouped time series.\n",
    "#                     If grouping is disabled, return the w-correlation matrix of the ungrouped time series.\n",
    "                \n",
    "\n",
    "#             Returns\n",
    "#             ----------\n",
    "#                 Y : ndarray of shape (n_groups, n_timestamps) if grouping is enabled and (L, n_timestamps) if it is disabled.\n",
    "#                 Wcorr : ndarray\n",
    "#                     The Wcorrelation matrix.\n",
    "#                     Wcorr will only be returned if ret_Wcorr is True\n",
    "#             \"\"\"\n",
    "#             N = len(ts)\n",
    "#             K = N - L + 1\n",
    "\n",
    "#             L_trajectory_matrix = hankel(ts[:L], ts[L-1:]) # (L, K)\n",
    "#             U, Sigma, V = np.linalg.svd(L_trajectory_matrix) # (L, L); (d, ); (K, K)\n",
    "#             V = V.T # (K, K)\n",
    "#             d = len(Sigma)\n",
    "\n",
    "#             deconstructed_ts = []\n",
    "#             for i in range(d):\n",
    "#                 X_elem = np.array(Sigma[i] * np.outer(U[:,i], V[:,i])) # (L, K)\n",
    "#                 X_elem_rev = X_elem[::-1] # (L, K)\n",
    "#                 ts_i = np.array([X_elem_rev.diagonal(i).mean() for i in range(-L+1, K)])\n",
    "#                 deconstructed_ts.append(ts_i)\n",
    "#             deconstructed_ts = np.array(deconstructed_ts) # (d, L, K)\n",
    "            \n",
    "#             if not perform_grouping and not ret_Wcorr:\n",
    "#                 return deconstructed_ts\n",
    "            \n",
    "\n",
    "#             w = np.concatenate((np.arange(1, L+1), np.full((K-L,), L), np.arange(L-1, 0, -1)))\n",
    "#             def wcorr(ts1: np.ndarray, ts2: np.ndarray) -> float:\n",
    "#                 \"\"\"\n",
    "#                 weighted correlation of ts1 and ts2.\n",
    "#                 w is precomputed for reuse.\n",
    "#                 \"\"\"\n",
    "#                 w_covar = (w * ts1 * ts2).sum()\n",
    "#                 ts1_w_norm = np.sqrt((w * ts1 * ts1).sum())\n",
    "#                 ts2_w_norm = np.sqrt((w * ts2 * ts2).sum())\n",
    "                \n",
    "#                 return w_covar / (ts1_w_norm * ts2_w_norm)\n",
    "\n",
    "#             Wcorr_mat = pairwise_distances(deconstructed_ts, metric=wcorr)\n",
    "\n",
    "#             if not perform_grouping:\n",
    "#                 return deconstructed_ts, Wcorr_mat\n",
    "\n",
    "#             Wcorr_mat_dist = 1 - Wcorr_mat\n",
    "#             distance_threshold = 1 - wcorr_threshold\n",
    "#             agg_clust = AgglomerativeClustering(metric='precomputed', linkage='single',\n",
    "#                                                 distance_threshold=distance_threshold, n_clusters=None)\n",
    "#             clust_labels = agg_clust.fit_predict(Wcorr_mat_dist)\n",
    "#             n_clusters = clust_labels.max() + 1\n",
    "#             grouped_ts = [np.sum(deconstructed_ts[clust_labels == cluster_id], axis=0) \n",
    "#                         for cluster_id in range(n_clusters)]\n",
    "#             grouped_ts = np.array(grouped_ts)\n",
    "            \n",
    "#             if not ret_Wcorr:\n",
    "#                 return grouped_ts\n",
    "            \n",
    "#             Wcorr_mat = pairwise_distances(grouped_ts, metric=wcorr)\n",
    "#             return grouped_ts, Wcorr_mat\n",
    "    \n",
    "#     def _get_dominant_frequencies(self, spectrum: np.ndarray, axis=-1, threshold=.5):\n",
    "#         \"\"\"\n",
    "#         Given the frequency spectra of one or multiple signals, compute the dominant frequencies along a specified axis.\n",
    "\n",
    "#         Parameters\n",
    "#         ----------\n",
    "#             sig : ndarray\n",
    "#                 The signals to compute the dominant frequencies on.\n",
    "#             axis : int, default=-1\n",
    "#                 The axis along which to compute the dominant frequencies.\n",
    "#             threshold : float, default=0.5\n",
    "#                 The threshold which divides dominant and non-dominant frequencies.\n",
    "#                 A dominant frequency has a peak of amplitude of higher than threshold times the maximum amplitude in that spectrum\n",
    "\n",
    "#         Returns\n",
    "#         ----------\n",
    "#             dom_freqs : ndarray\n",
    "#                 dom_freqs has the same shape as sig.\n",
    "#                 Iff a value in sig corresponds to a dominant frequency, this value is set to True in dom_freqs.\n",
    "#         \"\"\"\n",
    "\n",
    "#         max_amplitudes = np.max(spectrum, axis=axis, keepdims=True)\n",
    "#         dom_freqs = spectrum > threshold * max_amplitudes\n",
    "\n",
    "#         return dom_freqs\n",
    "\n",
    "#     def get_Facc(self, acc_window: np.ndarray, hr: int, delta: int = 10):\n",
    "#         # from actual hr ger hr_idx\n",
    "#         hr_hz = hr / 60\n",
    "#         hr_idx = int(round(hr_hz/100*self.n_freq_bins))\n",
    "\n",
    "#         _, acc_freqs = signal.periodogram(acc_window, nfft=self.n_freq_bins * 2 - 1)\n",
    "#         acc_dom_freqs = self._get_dominant_frequencies(acc_freqs)\n",
    "#         F_acc = np.logical_or.reduce(acc_dom_freqs)\n",
    "#         N_p = np.arange(hr_idx, self.n_freq_bins + 1, hr_idx)\n",
    "#         for idx in N_p:\n",
    "#             F_acc[idx-delta : idx+delta] = False\n",
    "        \n",
    "#         return F_acc       \n",
    "\n",
    "#     def filter_ssa_groups(self, ssa_groups: np.ndarray, F_acc: np.ndarray):\n",
    "#         _,ssa_groups_spectra = signal.periodogram(ssa_groups, nfft=self.n_freq_bins * 2 - 1)\n",
    "#         ssa_dom_freqs = self._get_dominant_frequencies(ssa_groups_spectra)\n",
    "#         group_filter = np.logical_or.reduce(np.logical_and(ssa_dom_freqs, F_acc), axis=1)\n",
    "#         group_filter = np.logical_not(group_filter)\n",
    "\n",
    "#         return ssa_groups[group_filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "29315bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# event = 0\n",
    "# raw_ppg_signal = pre_event_signals[event]['ppg_data'][:, 1:5]  # shape: (n_samples, 4)  # shape: (n_samples,)\n",
    "# # windowring\n",
    "\n",
    "# window_length = 10\n",
    "# i=5\n",
    "# raw_ppg_signal = raw_ppg_signal[window_length*100*(i-1):window_length*100*i]\n",
    "# normalized_ppg = (raw_ppg_signal - np.mean(raw_ppg_signal, axis=0)) / np.std(raw_ppg_signal, axis=0)\n",
    "# raw_ppg_signal = np.mean(normalized_ppg, axis=1)\n",
    "# IMU_signal = np.array(pre_event_signals[event]['imu_data'][window_length*100*(i-1):window_length*100*i, 1: 4])\n",
    "# t=np.array(pre_event_signals[event]['ppg_data'][window_length*100*(i-1):window_length*100*i,0])\n",
    "# hr_mask=(pre_event_signals[event]['hr_data'][:,0] >= t[0]) & (pre_event_signals[event]['hr_data'][:,0]<=t[-1])\n",
    "# hrv_mask=(pre_event_signals[event]['hrv_data'][:,0] >= t[0]) & (pre_event_signals[event]['hrv_data'][:,0]<=t[-1])\n",
    "# hr=np.array(pre_event_signals[event]['hr_data'][hr_mask, 1])\n",
    "# hrv_window=np.array(pre_event_signals[event]['hrv_data'][hrv_mask, :])\n",
    "# hr=np.mean(hr)\n",
    "# # 0.3-5hz 4 order butterworth bandpass filter in band ma removal\n",
    "# b, a = signal.butter(4, [0.4, 5], btype='bandpass', fs=100)\n",
    "# filtered_signal = signal.filtfilt(b, a, raw_ppg_signal)\n",
    "# # IMU filtering\n",
    "# filtered_IMU_x = signal.filtfilt(b, a, IMU_signal[:, 0])\n",
    "# filtered_IMU_y = signal.filtfilt(b, a, IMU_signal[:, 1])\n",
    "# filtered_IMU_z = signal.filtfilt(b, a, IMU_signal[:, 2])\n",
    "# filtered_IMU = np.column_stack((filtered_IMU_x, filtered_IMU_y, filtered_IMU_z))\n",
    "# # SSA on the ppg signal, attain groped ppg\n",
    "# ssa_class = ssa_process(n_freq_bins=4096)\n",
    "# grouped_ppg= ssa_class.ssa(filtered_signal, int(0.3*len(filtered_signal)), True, 0.3, False) # n*1000\n",
    "# # Get F_acc heart_rate=69\n",
    "# input_IMU_signal = np.copy(filtered_IMU)\n",
    "# input_IMU_signal = np.transpose(input_IMU_signal) # 3*1000\n",
    "# F_acc = ssa_class.get_Facc(input_IMU_signal, hr, 10)\n",
    "# # SSA_group filtering\n",
    "# filtered_grouped_ppg = ssa_class.filter_ssa_groups(grouped_ppg, F_acc)\n",
    "# filtered_ppg = np.sum(filtered_grouped_ppg, axis=0)\n",
    "# hrv_window[:,1]=max(filtered_ppg)\n",
    "# # # adaptive filtering\n",
    "# # adaptive_filter = pa.filters.FilterRLS(n=3, mu=0.99, eps=0.1)\n",
    "# # y,e,w = adaptive_filter.run(filtered_signal, filtered_IMU)\n",
    "# # SSA performed on the PPG signal\n",
    "# import neurokit2 as nk\n",
    "# filtered_signal = nk.ppg_clean(filtered_signal, sampling_rate=100)\n",
    "# peaks, info = nk.ppg_peaks(filtered_signal, sampling_rate=100,method='elgendi',correct_artifacts=True)\n",
    "# filtered_ppg = nk.ppg_clean(filtered_ppg, sampling_rate=100)\n",
    "# peaks1, info1 = nk.ppg_peaks(filtered_ppg, sampling_rate=100, method='bishop',correct_artifacts=False)\n",
    "# hrv=nk.hrv_time(peaks, sampling_rate=100)\n",
    "# # nk.ppg_plot(signals, info)\n",
    "# pks_idx=info[\"PPG_Peaks\"]\n",
    "# pks_y=np.ones(len(pks_idx))*max(filtered_signal)\n",
    "# pks_idx1=info1[\"PPG_Peaks\"]\n",
    "# pks_y1=np.ones(len(pks_idx1))*max(filtered_ppg)\n",
    "# plt.figure(figsize=(18, 6))\n",
    "# plt.plot(t,raw_ppg_signal)\n",
    "# plt.figure(figsize=(18, 6))\n",
    "# plt.plot(t,filtered_signal)\n",
    "# plt.scatter(t[pks_idx], pks_y, color='red')\n",
    "# # plt.figure(figsize=(18, 6))\n",
    "# # plt.plot(t, filtered_ppg)\n",
    "# # plt.scatter(t[pks_idx1], pks_y1, color='red')\n",
    "# # plt.scatter(hrv_window[:,0], hrv_window[:,1], color='red')\n",
    "# # plt.scatter(t[pks_idx], pks_y, color='green')\n",
    "# # plt.plot(t, filtered_IMU)\n",
    "# # plt.figure(figsize=(18, 6))\n",
    "# # plt.plot(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbb8467a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import neurokit2 as nk\n",
    "# import pyhrv.nonlinear as nl\n",
    "def poincare_features(rr_intervals):\n",
    "    rr_intervals = np.asarray(rr_intervals)\n",
    "    rr_n = rr_intervals[:-1]\n",
    "    rr_n1 = rr_intervals[1:]\n",
    "    \n",
    "    diff_rr = rr_n1 - rr_n\n",
    "\n",
    "    sd1 = np.sqrt(np.var(diff_rr) / 2)\n",
    "    sd2 = np.sqrt(2 * np.var(rr_intervals) - sd1**2)\n",
    "\n",
    "    return sd1, sd2, sd2/sd1\n",
    "def window_feature_extraction(window_ppg, window_t, event_hr, event_hrv, event_trigger, fs=100):\n",
    "    # normalized_ppg=(window_ppg-np.mean(window_ppg, axis=0))/np.std(window_ppg, axis=0)\n",
    "    # window_norm_ppg=np.mean(normalized_ppg, axis=1)\n",
    "    # window_norm_ppg=np.mean(window_ppg, axis=1)\n",
    "    # hrv & hr\n",
    "    hr_mask=(event_hr[:,0]>=window_t[0])&(event_hr[:,0]<=window_t[-1])\n",
    "    hrv_mask=(event_hrv[:,0]>=window_t[0])&(event_hrv[:,0]<=window_t[-1])\n",
    "    window_hr=np.mean(event_hr[hr_mask, 1])\n",
    "    window_hrv=event_hrv[hrv_mask, :]\n",
    "    # ppg_filtering\n",
    "    b, a=signal.butter(4, [0.5, 5], btype='bandpass', fs=fs)\n",
    "    window_filtered_ppg=[]\n",
    "    for i in range(4):\n",
    "        filt_signal=signal.filtfilt(b, a, window_ppg[:, i])\n",
    "        window_filtered_ppg.append(filt_signal.reshape(-1, 1))\n",
    "    window_filtered_ppg=np.concatenate(window_filtered_ppg, axis=1)\n",
    "    normalized_ppg=(window_filtered_ppg-np.mean(window_filtered_ppg, axis=0))/np.std(window_filtered_ppg, axis=0)\n",
    "    window_norm_ppg=np.mean(normalized_ppg, axis=1)\n",
    "    window_filtered_ppg=nk.ppg_clean(window_norm_ppg, sampling_rate=fs)\n",
    "    window_peaks, window_info=nk.ppg_peaks(window_filtered_ppg, sampling_rate=fs, method='elgendi', correct_artifacts=True)\n",
    "    pks=np.array(window_peaks.PPG_Peaks)\n",
    "    window_peaks_idx=np.where(pks!=0)[0]\n",
    "    # calculate hrv for the right beats\n",
    "    if np.all((np.diff(window_peaks_idx/fs)<=2) & (np.diff(window_peaks_idx/fs)>=0.3)) & (len(window_peaks_idx>4)):\n",
    "        if event_trigger != 6:\n",
    "            window_y=1\n",
    "        else:\n",
    "            window_y=0\n",
    "        # time_domain hrv\n",
    "        window_t_hrv=nk.hrv_time(window_peaks, sampling_rate=fs)\n",
    "        # frequency_domain hrv\n",
    "        window_f_hrv_l=nk.hrv_frequency(window_peaks, sampling_rate=fs, psd_method='lomb')\n",
    "        window_f_hrv_w=nk.hrv_frequency(window_peaks, sampling_rate=fs, psd_method='welch')\n",
    "        window_f_hrv_m=nk.hrv_frequency(window_peaks, sampling_rate=fs, psd_method='multitapers')\n",
    "        # print(\"Lomb HRV_HF is NaN:\", pd.isnull(window_f_hrv_l[\"HRV_HF\"]).item())\n",
    "        # print(\"Welch HRV_HF is NaN:\", pd.isnull(window_f_hrv_w[\"HRV_HF\"]).item())\n",
    "        # print(\"Multitaper HRV_HF is NaN:\", pd.isnull(window_f_hrv_m[\"HRV_HF\"]).item())\n",
    "        # non_linear hrv\n",
    "        # Poincare_plot\n",
    "        sd1, sd2, sd2_sd1 = poincare_features(1000*np.diff(window_peaks_idx/fs))\n",
    "        # Sampen1 Sampen2\n",
    "        Sampen1 = nolds.sampen(window_filtered_ppg, emb_dim=1, tolerance=0.2*np.std(window_filtered_ppg))\n",
    "        Sampen2 = nolds.sampen(window_filtered_ppg, emb_dim=2, tolerance=0.2*np.std(window_filtered_ppg))\n",
    "        # DFA1 DFA2\n",
    "        dfa1=nolds.dfa(window_filtered_ppg, order=1)\n",
    "        dfa2=nolds.dfa(window_filtered_ppg, order=2)\n",
    "        # # # shannon_entropy\n",
    "        # shannon_en=scipy.stats.entropy(window_filtered_ppg)\n",
    "        # # # fuzzy entropy\n",
    "        # fuzz_en=EntropyHub.FuzzEn(window_filtered_ppg)\n",
    "        window_data=[]\n",
    "        # label\n",
    "        window_data.extend([\n",
    "        event_trigger,\n",
    "        window_y,\n",
    "        # Time-domain\n",
    "        window_hr,\n",
    "        window_t_hrv['HRV_MeanNN'].item(),\n",
    "        window_t_hrv['HRV_SDNN'].item(),\n",
    "        window_t_hrv['HRV_RMSSD'].item(),\n",
    "        window_t_hrv['HRV_SDSD'].item(),\n",
    "        window_t_hrv['HRV_pNN20'].item(),\n",
    "        window_t_hrv['HRV_pNN50'].item(),\n",
    "        # Frequency-domain lomb\n",
    "        window_f_hrv_l['HRV_HF'].item(),\n",
    "        window_f_hrv_l['HRV_TP'].item(),\n",
    "        window_f_hrv_l['HRV_HFn'].item(),\n",
    "        window_f_hrv_l['HRV_LnHF'].item(),\n",
    "        # Frequency-domain welch\n",
    "        window_f_hrv_w['HRV_HF'].item(),\n",
    "        window_f_hrv_w['HRV_TP'].item(),\n",
    "        window_f_hrv_w['HRV_HFn'].item(),\n",
    "        window_f_hrv_w['HRV_LnHF'].item(),\n",
    "        # Frequency-domain milti\n",
    "        window_f_hrv_m['HRV_HF'].item(),\n",
    "        window_f_hrv_m['HRV_TP'].item(),\n",
    "        window_f_hrv_m['HRV_HFn'].item(),\n",
    "        window_f_hrv_m['HRV_LnHF'].item(),\n",
    "        # non-linear\n",
    "        sd1,\n",
    "        sd2, sd2_sd1,\n",
    "        Sampen1,\n",
    "        Sampen2,\n",
    "        # shannon_en,\n",
    "        # fuzz_en,\n",
    "        dfa1,\n",
    "        dfa2])\n",
    "        window_data=np.array(window_data).reshape(1,-1)\n",
    "        return window_data\n",
    "\n",
    "from joblib import Parallel, delayed\n",
    "import numpy as np\n",
    "\n",
    "def process_event(event, window_length, step, fs=100):\n",
    "    event_ppg = event['ppg_data'][:, 1:5]\n",
    "    event_t = event['ppg_data'][:, 0]\n",
    "    event_hrv = event['hrv_data']\n",
    "    event_hr = event['hr_data']\n",
    "    event_trigger = event['start_trigger']\n",
    "    \n",
    "    window_length_p = window_length * fs\n",
    "    step_p = step * fs\n",
    "    features = []\n",
    "\n",
    "    if len(event_t) < window_length_p:\n",
    "        window_features = window_feature_extraction(event_ppg, event_t, event_hr, event_hrv, event_trigger, fs)\n",
    "        if window_features is not None:\n",
    "            features.append(window_features)\n",
    "    else:\n",
    "        for start in range(0, len(event_t) - window_length_p + 1, step_p):\n",
    "            end = start + window_length_p\n",
    "            window_ppg = event_ppg[start:end]\n",
    "            window_t = event_t[start:end]\n",
    "            window_features = window_feature_extraction(window_ppg, window_t, event_hr, event_hrv, event_trigger, fs)\n",
    "            if window_features is not None:\n",
    "                features.append(window_features)\n",
    "\n",
    "    return features\n",
    "\n",
    "def set_construction(event_signal, window_length, step, fs=100, n_jobs=-1):\n",
    "    all_features = Parallel(n_jobs=n_jobs)(\n",
    "        delayed(process_event)(event, window_length, step, fs)\n",
    "        for event in event_signal\n",
    "    )\n",
    "    \n",
    "    # Flatten and concatenate\n",
    "    flat_data = [f for features in all_features for f in features if features is not None]\n",
    "    dataset = np.concatenate(flat_data, axis=0)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "54e766f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=set_construction(pre_event_signals, 30, 1, fs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5432adaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=set_construction(post_event_signals, 30, 1, fs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2a5fea5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "shape_train = (train_data[:, 1]==0)\n",
    "y_1=np.zeros(sum(shape_train))\n",
    "shape_test = (test_data[:, 1]==0)\n",
    "y_2=np.ones(sum(shape_test))\n",
    "y=np.concatenate((y_1, y_2))\n",
    "y=y.reshape(-1, 1)\n",
    "X=np.concatenate((train_data[shape_train,2:-1], test_data[shape_test,2:-1]), axis=0)\n",
    "\n",
    "tsne = TSNE(n_components=2, perplexity=50, random_state=42)\n",
    "X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "# 3. 可视化 t-SNE 结果\n",
    "plt.figure(figsize=(4, 4))\n",
    "plt.scatter(X_tsne[y.ravel() == 0, 0], X_tsne[y.ravel() == 0, 1], label='label=0', alpha=0.6)\n",
    "plt.scatter(X_tsne[y.ravel() == 1, 0], X_tsne[y.ravel() == 1, 1], label='label=1', alpha=0.6)\n",
    "plt.title(\"t-SNE Visualization of Train/Test Data\")\n",
    "plt.xlabel(\"t-SNE Component 1\")\n",
    "plt.ylabel(\"t-SNE Component 2\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e2605d40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9368932038834952\n",
      "[[94  9]\n",
      " [ 4 99]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yukunlong/anaconda3/lib/python3.12/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "X_pre=train_data[shape_train,2:-1]\n",
    "data_pre=np.concatenate((y_1.reshape(-1,1), X_pre), axis=1)\n",
    "X_post=test_data[shape_test,2:-1]\n",
    "data_post=np.concatenate((y_2.reshape(-1,1), X_post), axis=1)\n",
    "np.random.shuffle(data_pre)\n",
    "np.random.shuffle(data_post)\n",
    "train_data_pre=data_pre[0: 500, :]\n",
    "train_data_post=data_post[0: 500, :]\n",
    "train_data1=np.concatenate((train_data_post, train_data_pre), axis=0)\n",
    "test_data_pre=data_pre[500:-1, :]\n",
    "test_data_post=data_post[500:-1, :]\n",
    "test_data1=np.concatenate((test_data_post, test_data_pre), axis=0)\n",
    "\n",
    "X_train = train_data1[:, 1:-1]\n",
    "Y_train = train_data1[:, 0]\n",
    "X_test = test_data1[:, 1:-1]\n",
    "Y_test = test_data1[:, 0]\n",
    "\n",
    "sc=StandardScaler()\n",
    "X_train_norm = sc.fit_transform(X_train)\n",
    "X_test_norm = sc.transform(X_test)\n",
    "\n",
    "svm=LinearSVC(penalty='l1', C=1.0).fit(X_train_norm, Y_train)\n",
    "Y_pred = svm.predict(X_test_norm)\n",
    "print(accuracy_score(Y_test, Y_pred))\n",
    "print(confusion_matrix(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "be38c14f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9491017964071856\n",
      "[[153  10]\n",
      " [  7 164]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "X_pre=train_data[:,2:-1]\n",
    "shape1=train_data.shape[0]\n",
    "y_1=np.zeros(shape1)\n",
    "data_pre=np.concatenate((y_1.reshape(-1,1), X_pre), axis=1)\n",
    "X_post=test_data[:,2:-1]\n",
    "shape2=test_data.shape[0]\n",
    "y_2=np.ones(shape2)\n",
    "data_post=np.concatenate((y_2.reshape(-1,1), X_post), axis=1)\n",
    "np.random.shuffle(data_pre)\n",
    "np.random.shuffle(data_post)\n",
    "train_data_pre=data_pre[0: 800, :]\n",
    "train_data_post=data_post[0: 800, :]\n",
    "train_data1=np.concatenate((train_data_post, train_data_pre), axis=0)\n",
    "test_data_pre=data_pre[800:-1, :]\n",
    "test_data_post=data_post[800:-1, :]\n",
    "test_data1=np.concatenate((test_data_post, test_data_pre), axis=0)\n",
    "\n",
    "X_train = train_data1[:, 1:-1]\n",
    "Y_train = train_data1[:, 0]\n",
    "X_test = test_data1[:, 1:-1]\n",
    "Y_test = test_data1[:, 0]\n",
    "\n",
    "sc=StandardScaler()\n",
    "X_train_norm = sc.fit_transform(X_train)\n",
    "X_test_norm = sc.transform(X_test)\n",
    "\n",
    "svm=LinearSVC(penalty='l1', C=1.0).fit(X_train_norm, Y_train)\n",
    "Y_pred = svm.predict(X_test_norm)\n",
    "print(accuracy_score(Y_test, Y_pred))\n",
    "print(confusion_matrix(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "95570d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X=np.concatenate((train_data[:,2:-1], test_data[:,2:-1]), axis=0)\n",
    "y=np.concatenate((train_data[:,1], test_data[:,1]), axis=0)\n",
    "tsne = TSNE(n_components=2, perplexity=30, random_state=42)\n",
    "X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "# 3. 可视化 t-SNE 结果\n",
    "plt.figure(figsize=(4, 4))\n",
    "plt.scatter(X_tsne[y.ravel() == 0, 0], X_tsne[y.ravel() == 0, 1], label='label=0', alpha=0.6)\n",
    "plt.scatter(X_tsne[y.ravel() == 1, 0], X_tsne[y.ravel() == 1, 1], label='label=1', alpha=0.6)\n",
    "plt.title(\"t-SNE Visualization of Train/Test Data\")\n",
    "plt.xlabel(\"t-SNE Component 1\")\n",
    "plt.ylabel(\"t-SNE Component 2\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7c74c254",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threshold=0.5\n",
      "0.9273858921161826\n",
      "0.7417695473251029\n",
      "[[589  15]\n",
      " [236 132]]\n",
      "threshold=0.6\n",
      "0.7716049382716049\n",
      "[[580  24]\n",
      " [198 170]]\n",
      "threshold=0.7\n",
      "0.7788065843621399\n",
      "[[560  44]\n",
      " [171 197]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yukunlong/anaconda3/lib/python3.12/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/yukunlong/anaconda3/lib/python3.12/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/yukunlong/anaconda3/lib/python3.12/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/yukunlong/anaconda3/lib/python3.12/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/yukunlong/anaconda3/lib/python3.12/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "/Users/yukunlong/anaconda3/lib/python3.12/site-packages/sklearn/svm/_base.py:1235: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "# data_split\n",
    "Y_train = train_data[:,1]\n",
    "Y_test = test_data[:,1]\n",
    "X_train = train_data[:, 2:-1]\n",
    "X_test = test_data[:, 2:-1]\n",
    "event_test = test_data[:, 0]\n",
    "# datastandard\n",
    "sc = StandardScaler()\n",
    "X_train_norm=sc.fit_transform(X_train)\n",
    "X_test_norm=sc.transform(X_test)\n",
    "# l1 svm\n",
    "linear_svm = LinearSVC(penalty='l1',  C=1.0)\n",
    "linear_svm = CalibratedClassifierCV(linear_svm)\n",
    "linear_svm.fit(X_train, Y_train)\n",
    "# feature_weight = linear_svm.coef_.flatten()\n",
    "print('threshold=0.5')\n",
    "print(accuracy_score(Y_train,linear_svm.predict(X_train)))\n",
    "print(accuracy_score(Y_test, linear_svm.predict(X_test)))\n",
    "print(confusion_matrix(Y_test, linear_svm.predict(X_test)))\n",
    "Y_predict = linear_svm.predict(X_test)\n",
    "Y_proba = linear_svm.predict_proba(X_test)\n",
    "Y_predict_norm = linear_svm.predict(X_test_norm)\n",
    "Y_predict = Y_predict.reshape(-1,1)\n",
    "Y_predict_norm = Y_predict_norm.reshape(-1,1)\n",
    "Y_output=np.concatenate((event_test.reshape(-1,1),Y_test.reshape(-1,1), Y_predict, Y_proba, Y_predict_norm), axis=1)\n",
    "# threshold=0.6\n",
    "mask=(Y_proba[:, 0]>0.5)&(Y_proba[:,0]<0.6)\n",
    "Y_predict_1=np.copy(Y_predict)\n",
    "Y_predict_1[mask] = 1\n",
    "print('threshold=0.6')\n",
    "print(accuracy_score(Y_test, Y_predict_1))\n",
    "print(confusion_matrix(Y_test, Y_predict_1))\n",
    "# threshold=0.7\n",
    "mask=(Y_proba[:, 0]>0.5)&(Y_proba[:,0]<0.7)\n",
    "Y_predict_2=np.copy(Y_predict)\n",
    "Y_predict_2[mask] = 1\n",
    "print('threshold=0.7')\n",
    "print(accuracy_score(Y_test, Y_predict_2))\n",
    "print(confusion_matrix(Y_test, Y_predict_2))\n",
    "\n",
    "# print(accuracy_score(Y_test, Y_predict_norm))\n",
    "# print(confusion_matrix(Y_test, Y_predict_norm))\n",
    "\n",
    "Y_output2=np.concatenate((event_test.reshape(-1,1), Y_test.reshape(-1,1), Y_predict, Y_predict_1, Y_predict_2), axis=1)\n",
    "np.savetxt('/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/results/pangrichong.txt', Y_output2)\n",
    "# rbf kernel svm\n",
    "fs_svc = LinearSVC(penalty='l1', C=0.1).fit(X_train,Y_train)\n",
    "fs_model = SelectFromModel(fs_svc, prefit=True)\n",
    "weights = fs_svc.coef_.flatten()\n",
    "# X_weight_train = X_train_norm * weights\n",
    "# X_weight_test = X_test_norm * weights\n",
    "# rf = SVC(kernel='rbf', gamma=0.1, C=1.0).fit(X_weight_train, Y_train)\n",
    "# print(accuracy_score(Y_test, rf.predict(X_weight_test)))\n",
    "# print(confusion_matrix(Y_test, rf.predict(X_weight_test)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09d44695",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[16:39:43] WARNING: /Users/runner/work/xgboost/xgboost/src/learner.cc:738: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[124   0]\n",
      " [ 59  21]]\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb_model = XGBClassifier(\n",
    "    reg_alpha=1.0,   # 启用L1正则化\n",
    "    reg_lambda=0.0,  # 如果只想使用L1，可以关闭L2\n",
    "    max_depth=20,\n",
    "    learning_rate=0.1,\n",
    "    n_estimators=10000,\n",
    "    use_label_encoder=False,\n",
    "    eval_metric='logloss'\n",
    ")\n",
    "xgb_model.fit(X_train_norm, Y_train)\n",
    "print(confusion_matrix(Y_test,xgb_model.predict(X_test_norm)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdb8020a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/pang_watch_data/ppg_green_raw/250709/15.csv\",header=None)\n",
    "aligned_df=pd.read_csv(r\"/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/pang_watch_data/ppg_green_raw/250709/aligned_15.csv\")\n",
    "imu_df=pd.read_csv(r\"/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/pang_watch_data/imu_raw/250709/17.csv\",header=None)\n",
    "imu_df1=pd.read_csv(r\"/Users/yukunlong/Documents/working/ppg_modeling/test4Pang/pang_watch_data/imu_raw/250709/aligned_15.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6f1ecd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x38a31b140>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jiIiIiCIkSajQ/T+RP9hTHQGuyxKlqcfrXJ/NlFznOoj8wSW1iIiIiIh8Y1AdJvNwvsfjg+3pAddVXGP4SX7z4cE0iShgArikFhERERGRLwyqQ+iA0gIAcLllIt7Dtdrx7oUrgqqvTDUDAL6Qz6l744iIiIiIiCjkGFRHgFUMLtHBETUNAHBcTQplc4gCIES7AURERERE9RqD6hDqJOYAAL43T0Qv7NOO28S4oOo7STwCALjH8LPueFr20uAaSERERERERCHFoDpMpuI9j8ePCmkB19VMKNbti7IlqDYRBUplTzURERERkU8MqsOkKYq0bZPiTMm/1DAyGs0hCshk2/VYKvfDl3HXRbspRERERET1GtepjgBJtWvbCp9jUAyYKV+ImfKF6GHifH4iIiI6cVhUA8yCvfaCRC4Y4UXYVrFHtJtAREREREREIcKgOgJcs38Pl9dHsSVE/rlD+gWvG2agh31ntJtCREREFDHFSAAA2FQpyi2hWMLh3xFwILG/tm2CLXoNIfLTs8a5AICBFRkA7oxuY4iIiIgiJK0qQbBRkKPcEool7KmOAFFVtO1UNb/O9dmNiXWug4iIiIiIPCtWE6LdBIohDKrDZAFGeTw+Ul4XcF0larxu/3iLU4JpElEQuKQWERERnXgEqNFuAsUQBtUhtF9pCQC4wvICXsfN2vFuxSuDqs9aNZdjnnxm3RtHFASuU01EREQnoiShovZCRFUYVEeALAQ3df2A2goAkKM2CWVziIiIiIiIKEQYVIdQZ/EoAGC+eRJOwkHtuM0l+3cgI0lOEo8AAO4wLNQdb3Z0edBtJPLEJisoqWQSPSIiIiKiQDGoDpMpeM/j8WNC04DraiEU6vYle3kwTSLy6txpy9B34p84XmrRHefwbyIiIiIi3xhUh0kLOLN8G5VKbfsPaVQUWkPk26SiCfjRNAEbNm0AALxrvxSblU74Mf6SKLeMiIiIiKh+Y1AdAZJq17YV/sqpHuotHkQ/cT8MihUA8Jb9alxsfRn/mEdHuWVEREREkVOmmqPdBIpBjPAiwHUa9XahS0jrIwqFpkIJAEBQ7LrjAkd/ExER0QmEU98oGAyqI8AumLTtQcrWKLaEyLfE0v0AgOulxXja8BU62A9Gt0FEREREEVSIxGg3gWJQcGs9UUD2JQ0Gsh3bCeCad1T/vWz8FACwp3QngBui2xgiIiKiCGkr5EW7CRSD2FMdAaIqa9uN1aI61ydLCXWug8gzDnkiIiIi2qe0inYTKIYwqA6T3zHC4/HTlTUB11Wqxun2j7c6Lag2EQWK84qIiIjoRMR7IAoEg+oQOqC0AABcbXkOk3Gndrxryeo61TtfZhBNESLwK4GIiIioq5gV7SZQDOEddAhVL5elQIAQgrTJu5U2AIAjalqd6yLyJUtNBQCUJbSNckuIiIiIiGJLwEH1smXLMHbsWLRu3RqCIGDBggU+yy9duhSCILj9t3PnzmDbXG91ER3ZyL4wTUFnNUM7bhGDmwPdXcwEANxi+F13vGnOv0G2kMiz6iFOQo0F2zj0iYiIiIjIt4Czf5eVlaFfv3645ZZbcMUVV/h93a5du5CcnKztp6U13N7XOMGGl/EegNvczh0XGqNFgPW1FvJ1+wZbSfCNI/LgO/l0pKAMbcypuuMMqomIiIiIfAs4qB4zZgzGjBkT8As1b94cjRs3Dvi6WNUGx7Rto1Kpbf8onoNe0WgQkQ/9hX0wCzYUiUYAwCz7eThXWofF8eehR5TbRkRERBQpWWoqWgv5OK4moWm0G0MxI2JzqgcMGIBWrVrhrLPOwt9//x2pl60XJNWubSvs+aN6aKi4E8PFHZBkGwBgkv1mnGJ5D0viz4tyy4iIiIgi53XbNQCAbUrH6DaEYkrAPdWBatWqFT7++GMMGjQIFosFn3/+Oc466ywsXboUp59+usdrLBYLLBaLtl9cXBzuZkbMTqFztJtA5CZesAIABJcHQEREREREVLuwB9UnnXQSTjrpJG1/xIgRyMjIwBtvvOE1qJ4yZQomTZoU7qZFjCw4f80DlW1RbAmRbymF2wGMwKXiCqQJhciUz452k4iIiIgiJg8p+E/uhW1qR3iOVIjcRWVJreHDh2PPnj1ez48fPx5FRUXafxkZGV7LxoI9ycO07SSURrElRP552/QBnjXOxYPFb0S7KUREREQRM97wFUZK2/Gf0jvaTaEYEvaeak82btyIVq1aeT1vNpthNpsj2KLwEhRZ205GWZ3rU8SG87uh+katvQgRERFRA1W9vChXQKFABBxUl5aWYu/evdr+gQMHkJ6ejtTUVLRv3x7jx49HZmYm5syZAwB4++230bFjR/Tu3RtWqxVffPEF5s+fj/nz54fup6iHlmEQxno4fqayKuC6KlSTNucVAHJbn1mHlhH5ov8DwhCbiIiITiy8+6HABRxUr1u3Dmee6QzqHn30UQDAzTffjNmzZyM7OxuHDx/WzlutVjz++OPIzMxEfHw8evfujYULF+KCCy4IQfPrlyNqM7QV8nCDdTy2xg3UguqupevqVO+P8khca1ha5/YR1UZw+0PCp7RERER04ugpOqadfmB8B8DT0W0MxYyAg+pRo0ZBVb0/wZk9e7Zu/8knn8STTz4ZcMNikUU1AoLj/11/RWKQGZX3Kq3RVczCAdX7UHmiUNintEIXMRuVcc2i3RQiIiKiqIuHpfZCRFWikqisoeoiZgMA3je9i/Zqtna8UmoUVH1dxSwAwI2GRbrjqccCH0JO5ItS/VVQ43kZ5xMRERHRiYiDwCkQDKrDoLlQiMl4z+O5IiQFXF9bIU+3b7QWBtMsIq9+V4bga/soVJqb1jjDoJqIiIhORLwHIv8xqA6TjsjSto2Kc/jIPDEEc8n56IxCrLdwEGlCISrNjuHf38unolI14t/4M6LcMiIiIqLIKVXjAAAFQXSE0YmLQXUESC5zqhkPU32jKgpGS+kYLaVr8/8ftd2LHpbPsKjRRVFuHREREVHkTLDdCgDYrbaLcksoljCojrC9QvtoN4FIxzXxoKDKunMc+EREREQnIoE3QRQABtUR1kfdE+0mEOm4BtVNc9cAAM4V1+IKcRlS5PxoNYuIiIgo4gqQhPVKN+xX20S7KRRDGFRHwM7kU7Ttpmph9BpC5IGqKtp29TrVH5um4U3TDDxUODVazSIiIiKKuGcNX6CPcBC/YWS0m0IxhEF1BLjOqU5AZZ3rU0RjnesgquZr3XkiIiKiE4kZNpgFG6fAUUAYVIfJKpysbbuGLOeq/wZcl02VdPvHWp8VbLOI3LgG1TUDbK5TTURERCeS6lF7nFRNgWBQHUJH1SYAgDutj2C88JB2vFPpRm07mI/nT8qIujaNyCvV5bGPUCM/Pf+cEBER0YmkvZgLAJiK96LcEoolDKpDqFSNBwAUqolQBGfvskmpcCnl/1DbfUorAMBuhSn9KXxU0YSNSlcAgE1qFOXWEBEREUVfY6E02k2gGMKgOoS6ilkAgCnGmWitHtOOV4qJQdXXRcwGANwgLdYdb5K7NsgWEnmmaH3SNYZ/c+gTERERnYCYcYYCwaA6DLqI2XhRne7xXAkS/KrjyN6t2na7qmEo1cyW48E3jqgGVQVWKH3wszwcFXHNo90cIiIioohTFUW/z0lwFAAG1WHSFRnatlG1atuzhUv9un7RL/NC3SQij1R7BboKmRChoCipOwBgidwfALAxbngUW0ZEREQUfhv++ByFL7bHlmU/asfKEB/FFlGsYVAdAaLLklr+KlcMYWgJNQS7NyzFmh9ClzxDle24UFqDC6U1EFQZAHCr7Ul0rJyLP5IuD9nrEBEREdVH25f/gCYowd9/fo+HrfcCAA6hdZRbRbGEkVuE7Vfb+FXO1zwOlbM8Tmjdf7oEALC1eUf0OWVsnevTLalV59qIiIiIYotS1c+ouvQ3cvA3BYI91RHhDFVOwkG/ruhi2+vjLD/mBJRl7ghJPa5BddvsPwEAI8RtGCWmI14tC8lrEBEREdVX1UuKqgCK0AjblQ7IFFpEt1EUU9hTHQE7kk/FWcc+AwC0hH9Jxjr5DKqJQkfXU121+ZXpZQDA5uODAIyOQquIiIiIIuNi6T8AwEXiKlworkYzoQgTxTG4MsrtotjBnuoIEKvmqQKAGVYfJYn8VxbXMiT1uAbVHABOREREJ5oUoRyAY3ncxkIpmgilkASllquInBhUh8l69PJ4/EIs87MG78HN0TbnBdEiaigylDQAgNXUGACwcuYjWDXnueArVH0F0pxqQERERCcO51BwhknkP75bQihXTQYAPGW7A88ID2nHO5Wla9vbW/s3kOS42Ezb/kXmskbk9Jz9/3CP9SEUJ3TA0cN7MOLIpxi+/13I9sCzzDuwp5qIiIgIAJoJxQCACfIHUW4JxRIG1SFUqCYBAA6pLWAVTNrxeLlE25ZSO/pV12ZTP217q6K/xmfHIjV4lTCjFPFQRCMqXOJoJcheZcWUgjXKSQD4VJaIiIhOPMVqAgAgr6qDDACaq/7lQSICGFSHVDcxEwDwtOErpLl8ECukpIDrEhRntHSt9LfuXJPjG4JsITUE7xnfxeemqUisyIRrz/K2/xYGVZ8qCChX47yc5fBvIiIiathmyecDABbKw7RjKu+BKAAMqsOgv7gPL6rv16mOu8s/0rY7ijm6c3GVx+pUN8W2tKphSWZrAaA4k+D1X3JjUPWpqop0tQsWywNQEddcfy74ZhIRERHFBEV1BNCiy50Pg2oKBIPqMOmBA9q2QWHGbwq95LKDEFXniIZcNaXWa2b9ewD3fbkBdtmZ0TL/8FY8bPgeSUIFcpoOQ97Rw9q5nXH9Q9pmIiIiovqmegqda1DtOpWTqDYMqiPANfAJBZX9h1SlIj9L2/717EW1lp/083Ys3JKNhVuytWNJ314DABgm7oQKAXsWvqOd+z3l6hC2loiIiKj+6SgeBQDkqE3wsPVeAMBRIS2aTaIYY4h2A04MzuEjJY06hKA+BtUEJOz7FT22vwwAKFATAclc6zVfm15ES+RjW+4HANoAAFqqudp5xzuLw52IiIjoxKGojn5GK0MjChJ7qiPsWNMhIaiF/2wE9LVs1LZ/lEfi+P70Wq8ZJu5EB/EY4i05Hs93OvwdMhTncm4GlVMXiIiIqGETqvoTVAgoQxwOKC1wXEiNbqMopjA6i4CdSSOi3QRq4P7P8Cce3Xuz3+UTKo5q25uVTtq2oMi4OnOKtn9f7kuhaSARERFRPXWquAUAcKW0DE8bvoJZsGGW8doot4piCYPqiHAO13ZdKouoLg4pzWsv5IXg8p7crbZznqixCLrAqQZERETUwLUUCgA4lsdtKeSjtZAPSVBquYrIiUF1mGzCSR6Pdz6yoM51H209us51UOwrRGLQ17rGzv+zX+ByRnHMz64ux/nVREREdAIRPGwR1YZBdQgVqo0AAC/bxuEZ4SHteMfyLXWqd5E8SLevClKd6qPYVqLGA/CcTOPwnk1er8s7mqFtu/Y/N61a99pxQsVxNdm5L/APChEREZ04EgQLAOAR60dRbgnFEgbVIXRMbQwA2Kp2QrkQrx1PtOfXqd4NSrc6XU8NS/V61E2EMrdzFSVFXq/b+/1kbdt1WPdgYbe2raoKkoXyUDSTiIiIKCZU38MrqrMzoZXiOakrkScMqkOou5gJALhH+glNVGdwUy4le7vEL1dLf+v2m+R7742khq9z1VqKuUqK27nExj7WVFSdc4Nk0aRtnyG5vJ9UFc2FQudu8M0kIiIiignv2i8DAPymOFfpUTlajwLAoDoMTpe2YJL6flDXKrLsdqytkKfbjy/PDqpualhGSNvdjik+EuHlJnTRtvOSe2vbg8Q92napuWbyM/5BISIiooatOoeM6NKdwLwyFAiucB4mfbBX25YUm1/XFBXkofKdoTiUOhJDXY4bBX2grbL7kLzw9FCmWkFcW227LK6lxzIH0s7W7R8098CA0DSNiIiIqF5SqgLoMdJa7ZgM5jAi/7GnOgIk1b+gevuC19ECxzE0/+cwt4gaKtXXkm2i44/DfqUlrIYkj0WUGk9sfm1yfcjaRkRERFQfjRDdR/8VCE2i0BKKVQyqI8B1+EhpQhuv5Voc/CkSzaEG6gLLK6hMbO/1fIUhFYBjTnazIs8Z6eWawyC4rjoRERE1cKKnLDIc/U0BYFAdYTnNRng9lyG29XrOlSrwn43cbVc7wi6avZ5vXrlP24635Hos0+/w57p9QbaGpnFERERE9ZY+qM5RG6NE8Dyqj8gTRmcRsCtxmF/l8tufBwDYp7QKZ3OogchXE7XtRfJAAO7Dt125LqOVUHFU285Um2rbolypu+auvCl1bicRERFRfXaysF+3X6rG44P4u6LUGopFDKojwN+U/Eca9cYM+0X4Wh4V3gZRgyBDwhz7OQCAAeJe3C/9ALEky2v5eLnE4/GdisuQcZdltwDArHDNaiIiImrY2ov6EXxdxGyIULyUJnLHoDpMtqOLx+NdDn/n9RpJseBuwy+4y/CLz7pzWo6qS9OogUgTirTe52ZCMR43fgtDSabX8snWHJc9Z6/1R/aLtG2xzLUMIDDVPBEREZ2ION2SAsB3SwiVqnEAgPftF2OC+JB2vH25e0ZBT2TV8c8h1JjXsVTup9tXRGNdmkkx7ifZOS//RsNi3TlF0S+plVtciekTbsKSr9/V/3FwCZbNgjM7/bDjP+quL/OR+IyIiIioobqzYma0m0AxhEF1CGWqzQAAK5S+KHJJbtDYdsyv65tVOuZzpAqlOK46r1+t9AxhKynW7VTaeT1XtE0fZH/72h243/AjRu94zusT1z7CQa/1qUx9SURERA3YyjnPeTzeXs6IcEsoljGoDqGTxCMAgOulxUhSS7XjZYZkv67vXLpB23btrb5KWqor17hwa/CNpJj3pPEbr+cGHf5Ut3+ptMK54xJUy6JJ237KOC90jSMiIiKKISP2v6ttb1C6atvsWKBAMKgOg4uk1XhB+SDg6wSXJFGpgjMobykU6MrFl/LJGXm2Te2kbedlHUJrId+53+gkbXu/2tprHYrKPyJERER04tEnJwvN/dC6nz/Cvs3/haQuqr8YVIfJAOzQtiVV9lHSSVA9ZxlMECwhaRM1fEbYte09C9/SnTue7Ayqr9z5iNc6RME5SuK4qU0IW0dERERUf/UXnUtrKSFIVLZh/SoMWPcUyr+7p851Uf3GoDoCjIqfQTEzLVMtVMX38g6NXUY4rJD76M6V71+tbYvw7722vPm4AFpHREREFFv2Ka08Hi8XEupcd0Z2DiRBRQrK6lwX1W8BB9XLli3D2LFj0bp1awiCgAULFvh97b///guDwYD+/fsH+rIxTYakbZfHtfBarlKMj0RzKIaptTx4yUl0JrUrydiiO9f86FJt2wJDSNtFREREFIvesF/t8XgoBn+rVb3drqMAqWEKOKguKytDv379MH369ICuKyoqwk033YSzzjor0JdsULKbn+b1nF1wJI/6Ux7ksw5VlHyep4bLU1Cdg6ZYnnwhAOBYcaV2/Bppqa7cEHG3tp0sVISlfURERESx5DdlGJbI/d2OVwpxda5bUqwAgFY4Xue6qH4LOKgeM2YMJk+ejMsvvzyg6+666y6MGzcOI0aMqL1wA7Mrcahf5QTVXnshOqGpHubdrzrrO0glmQCAMdJa7Xhv8ZCuXIHYJODXG5P1fsDXEBEREcWKXsJBjJbS3Y6/nfJEnetOK3as2COxp7rBi8ic6lmzZmHfvn144YUXIvFy9Y7sZ6KDtSnn4yP7hchRAw9+6MSgQsB0+yW6Y7aE5vgLw2u9dp259jI1xcmltRciIiIiilHniOs9HhcFroZC/gt7UL1nzx48/fTT+PLLL2Ew+DeP02KxoLi4WPdfrNmNjh6Pd874wes1eYYWuMuwEDcaFvus+5iPIeTUsKmChDfs1+iOGUQBR5EKANimdPB4Xblqhmgwhr19RERERLHkEeN8L2fqHlSrtsraC1GDENagWpZljBs3DpMmTUL37t39vm7KlClISUnR/mvXrl0YWxk6FapjTvSX9rMwQXxQO96uYpe27WuIt+zln2Ol3EtfTjLXpZkUw1SoaIoi3bGUikPooGYBcBnyXWPudYJgQaOS/fDErnIRACIiIiJX40o/q3MdIw5+EIKWUCwI6910SUkJ1q1bh/vvvx8GgwEGgwEvvvgiNm3aBIPBgCVLlni8bvz48SgqKtL+y8jICGczQ+ag6sjsvVAZhuOCcwh3mjXTr+s7H1ng8fhypW+d20YNg6ooOF3crDuWUrwbfbFbd0yW3ddGvwaLtG2r6kx2t0uNjYdWRERERJHSyb4v2k2gGBLWdXWSk5OxZYt+WZ8PPvgAS5YswXfffYdOnTp5vM5sNsNsjr3e2J6iI/i/SFyJw6pzaaNSQ2O/rr+34mOPx6+Qlun2U4p2AF6Gl1MDZ7dgmulD3SEBwBBs1/atlkooiutCbu4y1OboUrVdM6EZERER0YlIVgUtqZgSmdRT1EAEHFSXlpZi79692v6BAweQnp6O1NRUtG/fHuPHj0dmZibmzJkDURTRp08f3fXNmzdHXFyc2/GGZJzhbzRTLQAuDkl9zQT9cN/EUgZB5KQoKg4NGo9m658CAKz+5jUMvOQBn9ccUdO0oNqT42oSmgolIWwlERERUf1yYPtauHbx6bJ0M1EZBSDgRzDr1q3DgAEDMGDAAADAo48+igEDBuD5558HAGRnZ+Pw4cOhbWUMGqJu1bZFl2WQPCwzXKsUoTwUTaIGwNM61VBldBhygbZ7YNdmrNh91Gc9Z0ibfZ6vDqgLjc0DbyQRERFRDOj0zdlez4UipF4o+7esLsW+gHuqR40a5fnGvsrs2bN9Xj9x4kRMnDgx0JeNaUbVEu0mUAPhaZ3qSlNjSJIzs/dNhkV49YemOK+Oyb5vsz6GZs0vxbl1q4aIiIjohDTJdjMulNbArorhnXNLUcfJAhHgmtW70pwWxZZQrPP0QEsRjG7LZT1lnFdrXUeLfC/zoIbkGS0RERHRial6XrZBUHx2SlLsY1AdYZktRtW5Dlngs64TlacvZLsqwRDEGtRXv7/U92sxqCYiIqIGrFSN83pOEXylfPVPb/Ggsz7FfbQhNRwMqiNgd6PB0W4CNRA1g+of5ZHo0KM/DPGJXq+Z3eZFj8efqJjm87VmmV7H+UdnBN5IIiIiohiQKHgftfd62it1rv8z06vatmy317k+qr8YVEeAXTBFuwnUQKiSEX/L/bT9nY2GoXO7tjAbJOxTWnm8xtzU8zrUAmofhtTIXhBcQ4mIiIhIoygMqhsyBtVhsg9tPR7vlPmT12uWyP39qjs3bUQwTaIGQJXi8KDNuVxW5ybOqQBmwebxGnNqG4/HL5JWh7ZxRERERDHksOI915G9yPdKKoFiT3XDxqA6hGyqY+7FL/JwvCA6A5/WlXu0bVH2nglc8TKHdb3STbdvNyTUpZkUy1SgBM5//w4V27TttkKex0suOMX39IN0xdeK1UREREQNk7d7JwD4qvimkL6WrMghrY/qFwbVIbRXdfQIfiWfiaOC88lXS+tBv64/W9ro8fhf8sA6t40aBlWV0QL5zgNC7cnE4owSDguee6sBJiQjIiKiE5MohDcj9+rGF2rbimgO62tRdDGoDqGe4mEAwOniZphUq3a8VGpSp3ovl5br9pOL93gpSQ1eRSFWx92v7SYMvk7bXtz5abfia9EbAKBeO1c7lq/qk5oNEPeGupVEREREJzzF5Ljn+ksewNV7GjgG1WFwl2EhJqiBZ02uVD0vi9RUKNbtJ5fsC6pdFPtcs38fuHox+pwyVtsXu5zhVt5+wVsAgEYpTbVjqUJpGFtIRERERAAgqI5ltM6SNiIvJyvKrQmdkqJ8bF22gPPEXTCoDpORarrLnn9DS3KkFh6PN2EQRFVU1bnGYcceg3Tn0jr0divfu+8AAECzlu2wYfg7WNnu9oBer8SQGkQriYiIiCgn3pm3Rq1sOCuqHJl+EfosuRlrv677smMNBYPqCDArFX6V82eJIzqxufZUC6L+49u3bYpb+eR45/ydgef/H5r0OU/bn2M/x618gZqInYpjCa5x1mfwe4s769xmIiIiovquWI0PeZ3rm47FcTUJABAvNJxEZT1tjkS5bfd+iYqykii3pn5gUB0BrnMoLCbvPX+CyqCafFPr+h5xCcQ9ZZuXoGgPd5jAjIiIiE4UFniehlkXiqpCqQq3lAaY/butehTG19qjspyjahlUR9iRlmfVuQ5FDP2HnmKFI+BVVM8Bb/njjmR5+5WWeL67+5roguD8yE+3X+Z2Plkox0nikVA0lIiIiCgm2FQJ5Wqc7tij1rvrXK+qAnJVuKU2oKD6gOKcsmoQFBzZsymKrakfGFRHwN6EAX6Vq+4hXNNvMlY1vyacTaJYpVT3InuWkJiC/27chw/6foPHLh3pdl4QJW37U9NrPl/qK9PLOPfYJ0E3lYiIiCgWGAUZHcRjumPfK6cHNUIwt8SC95fswbGSSozNeAMtBcdc6oYUVDcWynT7tsTWUWpJ/cHc7hFgFeNqLwTgB+MYKOUFOLP7KFgkI3Ds6zC3jGKNYojDV/YzIQgCrvVSZmSXZhjZpZnHc67zsJNRXuvrpdhyg2kmERERUcyYZx+Faw1L3Y7LigqDFNh0uC+m3oNHjPPxfysm497GzkC6oQTVK/ccwzDog2qYk6PTmHqEPdVhcgittG3Xp1wdshZ6veY748WYZr8KtsS2PuvOazqk7g2kmKSYUzDefgcmyHcEdb3r8O+OYk6omkVEREQUc1bIvXFQaYGflRFu5/aab4CtsszDVb49YpwPABhn+8Ex/ruKqjSM5ac2rPsXolCjB1/i1FQG1WHygviAtt3Ksl/bNti99w66jjBRJWfW5i1KR105qzGp7g2kmKRWDfwWgswhJjdyX7ZtjXJSXZpEREREFJNaCfnoKOYgHla3cwZBgc3mftyXPenLte0lygCgainUZXJflDXuXrfG1hO9GhW5HyzOjnxD6hkG1WHwqzwUR4SWAICM/HLYM/2bvN9ayUYXIROCvRJZrc9FhpIGAPhNHhq2tlJsURUFyShFUs1hN/5en9gcv8nOkQ6qovjM8s1l3oiIiKihaiRUAgA6CEc9npcDDKpLcg5o21ONM7XtlUpvyA2kUyzFLLkdE0qY5JZzqsPgZHE/JNUxb2LGNz/hWelfv657yzIRrc052FnQBZB6oJ3omM86VlqpK5dUsg9A55C2mWKDWJKFzXF3wqIaAVwV+PWCgGK1kbZvs9kwTNwZwhYSERERxYbqJGLPGb/0eN5uswRUX81500JVT7UCoTrXbMzzNIxdlW1RaEn9wp7qMGgr5OFP+VYAQKvSbUgQAvtA1pQm6IdZJBfvrlN9FLvUqi/nYElyJTqKzqexR49m1rVJRERERA1SoEG14hZaOSLpG6TFMBQ3jN5cTwnXZDuDagbVYVIz1bw3qqIgY88mqIo+WEo79p+23UwoDmnbKHZVz7sP9mGnoSRT1zO9/6dXdeeXmkfBojoHsJRLDWOoEhEREZE/rKpzeHOgw79FVR9wZps7AQDaibmIK9hV98bVB1VBdYbQGgVw3CeqDKoZVIfbGZV/+zwvvNgE7b48HbtfGYHqUEmAALM1PwKto5ijVq9THVymMlHUX9cxb6m2/Z16JsxXf4qjaioA4HLLRPzU4v7g2klEREQUQzYmjMSWuEEwPJ+LQiQCCDyoLkzpoW0fQyr+Sr0W6UoXAEDnv+9BXtah0DU4SlTZEVQXmlshT3IkwFXkwH5PDRHnVIeZGfphIzYvSQpOsu9EltA8Ek2iGKbWMagWBH1yiY4uiTkaoxRGSdCSkwX7GkRERESxZsCTv2nbG4XeMNjL0FIwBVRHaaMO6FL5ORJgQZOkBPRXAbmqD9MMG3Z/fjuaPbUopO2ONLWqN16FCEVwhJKKvWEsF1YX7KmOsMOtzq+9UC3rJcliYB9wakjqNqdaEPVBtc1liNNwbEU8LGhflSCPQTURERGdCHYo7XX7E8xP40bbMyhv1C6gehRVhQwJJUhAmWKq2neGW8mW2F966nCLs3GaZRo+S3sMclVQzURlDKrrldbqMQCAEOwixNTw1bGnuqJEP62geqg3ABwyd0dipfPL/nvTCzg797OgXoeIiIgoVtig73QwSI77LLsSWGeGsSIPg4Wd6CJkwioruOHoVAwVnXOp/zCeVffGRplFSkCG2gIlxjTMbnw/LrNMwtGmXP6Xw79DaHWTsRhW8LPuWCAJpRbJA3FQbYmR8WkAGkgyAwopxdAIP8ojoYgmXBbE9fYafzSql20DAPWMJ2CMcy63JQoqmlmZHZyIiIgaHlV1dlH8Ig/HyS7nDFU5aGxyYKlhm+csx3fmFwEA3ylnwlgjKBeDTjVbfyhVa4NJooDsuM7YqCaj0pAS5VZFH3uqQ0iJa+x2bJ1piG6/ffbvXq+fYh+Hl+03wJbYxufr5DUZGFT7KPbZGzXHQ7b78TzuDer6dj0Gux27xzwF46zPwN7iZEgGPmcjIiKihk9Vgd/lITiotMBmtYvu3LTyZ7DZfBsSMv8NrFKX5aYuEZZBUPVBtFjHaXz1QdP8DXja8BWGlC6BUXKEkvYAHz40RLyDDqFdqWdiRPbnAIA/5ME4D0C+2FRXxmRzLo9VVJAH1+c65aoZACAAUEXnP81upQ26i84eQ6upcaibTjGiOlFZsNOdpRrZv3cpbbFV6oEMpQKqKRmiqF8TnV+RRERE1FA1EUrQUcxBKvTL18bDgmShAqqtMrAKVWfQbBRk3T4ADJA3B93W+iK1aDsuMPyM9WVlKFPj0EXajsQCCUBg888bGvZUh1B2Yi98anckIturtgYAFAnJXsvvmvOQbv8k8QhaIw+CbMHRtmNwRG0GAPhRPiVMLaZYo6oqjLDDiOCyLEoCMNV2LT60j8UE2y243/ag7nzNRGYCw2oiIiJqgFQASagAALQQCnTnnFmtA1sqSq053FvVJ/DaYugdYCvroareeFWQMLJsEZ41zkVqfnp021QPsKc6xGomkMoWWyNfTUSqUOpWtn/+b7oex1eNH6OlUIDd+T9DMHRFWyEPADBGWqO7Lqn0AAD9MBU6MRgK9mFP3E1V6ycGPt9ZEoCnjfN0xybZHoFFKoahojvEuIQQtZSIiIioHlMV9BId60a/YPwcwHTtlElx9FBbCwPM1q3Kul1J0QflR8S2gbezntGW1BJEoOrhAxRm/2ZPdQilVhzCbQbHGnf3GX4CAPSyb/MYUAOASdB/8JrBMfRWEADBJdqu+fSscfGOkLWZYoxat7k4oiRBUfUPfl6wTcMrxk9gKj0CSeJzNiIiImr4avYqu+qkHAQADNs+OdBKdbtSjWBTbgihl+IYLakKEiA4fh41wB79hqgB/MvWHyfn/hJQ+WeavqPbNwjOD2Lq8Q3adpqgn+dKJ7A6LqkFOLJ6eyIIAmBK0AXdVtEc9OsQERER1V+hn+Km1giqs6RWuv3O9v0hf82Iq07GJkpIKTsAAIjL2eDjghMDg+qQcg90TrMs81r6jtxXvFQjwGw9HqpGUQOian8AwrOWuWSKR25V+rwLLK9gfqvHwvI6RERERNHkK6SeYrofAPBIs48DqvNo8sl4zXa1tj/bfD1+kkdo+6fYVwZUX71U9eBAFZx5eCyNfK9cdCLgWM8wi1crdPt2yTlntZOY4+NKPu8gd6rWU11325UO2KJ0wjWGpdoxUXCG6yoEpv8mIiKihkn1fpOTMvIWdPx9JK5qEdgc6Nyk3vhAFvGdfAZskNA0MaVhDPl2IVT3VAsi8pv0A/L2QjU1im6j6oGG9a9cj1Rn7q7pYJuL/Lpe8NERKXNI7omr+ulgHXqq1yWfjf1iR9wkTcFT9ju196oKASJUNBcKAQAiI2oiIiJqoFxj6gXySN25vkcXYJX5PlyS+WZAdSpVlR5DExQgGRYFUFzCrYawqsr6FlfgfMtU/NvyJm0JYEEJblWahoRBdZgcF5rU4WoBqLG0ERHg/ANQl6B68KPz0WnCRvSybMLt0kIty7wAQHTJWvm96XmMzvuyLs0lIiIiqpdUOOc/22sM3jWqNrQUClB4/GhAdZoqc9FbOIhWcEzjfL7yDVwhLdfOr5MG1KHF9UOJoSl2qu1RFtcC61tciSssL2B92mXRblbUcfh3mHRClsfjai1PqL60n4XB8akQhCNey8T+My4KlmxMxJ/yIFgNSfBvzINngijiWulvXFBjuTZJcj7MMQt2tLAerMOrEBEREdV/q5UeuNJl/1ChFcMBGCF7u8SjHkd/wkLz+wCAz+1no5tyQHdeRN1WcakPqnvjJVFAcXw7rFdtGGRuVctVDR97qsMkB6kAgPWmQbrj7Y4u9nnds/bbYEts43P4d36T/nVtHsUoa1Jb3Gl7DC8aHqhzXa4B9e3Wx2BN7giRIySIiIjoBKAKBnwnn44DSgvkq0m6c/Hljs6t86R1gVWqOIPwGw2Lkabm606LdVwatT7onPc3HpC+R4fyLVoiHkVhlx+D6hDa2+RU2FRHULIMAwEAOWILXRmz1bnm9Mf2C33U5gxuDinNdWcqzal1bCnFqurh374eugRqu9IBi5VBkM0pEER+JRAREdEJQBBhhhWdxBxtKly1ZjZfyYR9qJH8zAj9OtWX2H+D3RbbazoPPfgRHjN+hy7lW9CudAtulX5D+2IuqcU76BDKTOqH2fJ5AJyJCEoFz9nwZLsdg8VdWCb31R1vjBJAsaOgzRlaAqnv5NPD2GqKRUIIl9TqJR5y1BmeVbqIiIiI6qUkOFbpSRbKdceFJu2Dq7BGT7RJcB8+vn5m3UcbRpNVjAcAqBX56FL4L543fo7uhf9EuVXRx6A6zA5L7ZGtuvcs705fjoHiXpwubdEdT4+7C3EFuyAIAprD0at9lqR/+tOo9HD4Gkz1mjl3M/aab8B86z0hrfcycTmkSsf7zapyCDgRERE1cLINo6RNAID7pAW6U50ueNhZzB5AZmu19jnYw3Lm+V9fPSRUzQs3tuqL6vHfQtXDhGPFlXjzz13IKqzwdnmDxaA6hFIqs3CH4VcAwCVYCgDoZ9uEVkK+W9nEFN9DuAU4n261qTEkJbVoa90bSzFJVRQYBAVSCBJdrOr8oLY9zfQhTCUZAPRLPxARERE1RKrsDJYNNRKSNUp23qfbbJYAKo39OdO1UQRH54tkjocgVN0zVv3cd32xHu8t2YsbPlkdreZFDe+eQ6j/sR+0bS1boJd5+9XzKQqQ7HZOAJBYuEvbTxOKQ9ZGinWqy//WTeOeZ3g8Xop4bZsBNhERETVErktqbU46TXfOaDRp28VlAfS6KvqgulhNcCvyoPV+/+urhwyKI4YRTfFQa8wdbHRkOSYYPkeP439Fo2lRxTvmMJmG6wEAI23/eTxvt1YCAGxeVjUzWY6Hp2EU09SqL2tVqPtHVzTG6Q9UfTHmXfAJXk+ZgI6VczGv9fg6vw4RERFRfdbhxum6faPJeY80a+kOv+s5lDwI0+2XaPvP2G7T7QNAFzEzyFbWDwbVEVRLxjigRk/1ZdJy3G74DeOkEy+o5jrVYZaolun2ZdHx5EuuDqoFIyyKEWbBmR1QFQTfWaNUpq0/UalVX1qheAdUxDX3eLzHsHOxwtoVWOj/HxEiIiKiWKK6LAOV0ChFd04yOEOkjtm/AtD3ZHuzJ2koPrGn4XP7OTAKMvLUZHQTj+jKXGtYFnyj6wFjVVBtMCdoQXX1nOorpBUAgFOlbdFpXBSxpzrCDrR1PK2yV83PsAsmWGDUF1JkCIL3ZFGyFOf1HDVwavXw77qn6m7VRp/ZUvDwIEflAxwiIiJq4DzdA1VLUP0f/i1XBeo5SMURNQ2VMENR9eFWKO7houlhw7O40PIylLReLkd5v8igOkwugu+nUKUJbTHFdh1+jLvELY1/xs71Pnuq+bY9gWlBdd0/us2Taj6ccb7nWmT9hf8Z30DXvbPr/DpERERE9Y3q5x31lpxKv8opsox4Sx46CdlIQSkAYIZxGh4xzteVE2L8Tn6PvSW2qZ1gSkjC3hbn40br01jR9KpoNyvqOPw7TLrB97JXRcY0/E++ECc3SsWD5fp5HKVxrdFC5PMOcmczJmG53Adl5uZoF+rKXR7kbN6SjmcNG9DfthfAO6F+JSIiIqL6w0Nn1p/yIJwrrUeZSwJXb7Yu/xEdF9+F+6HiKXMldiltsVTph/Olte4vFcNBtaqqqLA6kjHHGSUUx7fDcqUcrcxto9yy6As4clu2bBnGjh2L1q1bQxAELFiwwGf5FStW4JRTTkHTpk0RHx+PHj16YNq0acG2N2YcVxsDADYYBuiOt8n5G6qioNU3F2KhaTzMonvq/d2Fqs9hKPmN+4S0rRQ7yht3x422Z/Ba/EMhrfe4mgRbUhtt/1nDFwCYeZ6IiIgaJlWKw+WWibjcMhGQzG7nz5XWAwBeNn5aa10nLb4FiUIFGgmOXu2TxCO4y7AwpO2tDyyWStwvzMM90k9IMakQq+IVX7MFiytt+DE9E2WWANb7jkEBB9VlZWXo168fpk+fXnthAI0aNcL999+PZcuWYceOHZgwYQImTJiAjz/+OODG1ncHGg/Xtv/GYABAptRaVybekofiwuM4WTyAnmIGGpfuQ7aqX7O6awoguAzFrXm+Mi4t1E2nGFH9nSX6SmQXAKvqmLvfVCiBam6iHd+odA1J/URERET1kSpK2KB2xwa1OwTJey4jfxQgye+yImJ3LevK0iI8aFiAp4zzEG8yolnZblwrLUGn0g1er7l/7kY8NC8dz/ywJYItjbyAg+oxY8Zg8uTJuPzyy/0qP2DAAFx33XXo3bs3OnbsiBtuuAHnnXceli9fHnBj67vDyYPwkf1CAM6hHRWC+5ARa6UzI/ht1i+wTD5Zdz4+zoSy5gNxXHV8QL+yjw5XkynGKFWPAkOV4mKP6hyu4xqnDxD3hugViIiIiOo3wcOdVaVq9FDSs+r7f3+YYKu9UD1VWV4CAJBVAQajCW0LVmOqcSaGFP4KAFgsDwQAfGIfo13Tft9cHIwbh5FbJ0a8vZEU8Ym7GzduxH///Yczzjgj0i8dETUz+u2VuuCg0kJ3rPoNCQD9rOn4od1T+Njlw9ik/BAkgwHmqg/dadJm3fWNyrNC3WyKEclHV2Oz+Ta8XfZkSOorb9JD2xYtHOpNREREJwbVZsFt0kLcJi0EFPehyVuHvAIA2Gns5XauJmvNlXy8+EUejvPtbwbW0Hok96BjqSxJqOrkqeqREao6fb5UzsFk2/X4UR6pXTPZOAsAcI1haQRbGnkRC6rbtm0Ls9mMwYMH47777sPtt9/utazFYkFxcbHuv1iQaD2Guw2/AAAuEhw98UNs69BRzNGVs5Q5fx6zYMPb1w7E+y4LwwsGEyRBQGLVvIwugj6IblqQHo7mUyxQ7EgWKpCg+peJsjbiSc4niaayTG17VfcnQlI/ERERnXgy929DUX5utJvhm2zBc8Yv8ZzxSwiq7HZaMiUAcK7B7MvDhvm1lgGAV+3XIFtOjtklS2Wrfnkx52o0jp9nudofM+ULsVntEuGWRV/Egurly5dj3bp1mDFjBt5++2189dVXXstOmTIFKSkp2n/t2oU8z3FYDDr6rbZtrloY3dMCWLbKUt1+y5Q4/GB+QdtPSOsIk0tvdKqgL08nLrXqiz1UX8XZhc6pCBCcXwfNep8JADiG1JqXEBEREXmVdWAn2swZiaR3ukW7Kb4pvu+mRIMjeZmk1j5cu6lQUmsZwBGEqipgr+W16ytBdPRM7zE4cu84e6od96edhSw8YvgWN0iLotPAKIpYUN2pUyf07dsXd9xxBx555BFMnDjRa9nx48ejqKhI+y8jIyNSzQyZ93AtAGCYzT2Vvq3c/YPXWcjWtnuNGAOTLTZ65ynCqtepFkLz0W3e/iTPJwxxOKQ0R7bApHhERETkv8zNSwAAohA7gaOnVXdEgwkAIHnoxfbXXPuZeNJ2h7b/tGEuJhpmo7w0Nu/zFdnxu1BQldhN0PdUnymm4yHDD7jb8LN2TU7Vikh7FOcqMw1RVNapVlUVFovF63mz2Qyz2T21fSyxVf1qU5Qi3XFVkGC31N7zLIpcQpzcOXuqQ5OqrNeQM4HFjm2r7PzjJzftjrOtb6NpIxPWh+SViIiI6EQghOjBf/j5HtYtGs0oVuNRgcBjkim263CWtAFZajMoLn2YF0mrAQCZxzORktI44HqjzS7GYZ/SCsel5gDce6ofEecBANoKedo1W4QeaIFV+Ew+F5Mj3N5ICjhyKy0txd69zszABw4cQHp6OlJTU9G+fXuMHz8emZmZmDNnDgDg/fffR/v27dGjhyMh0ooVK/DGG2/ggQceCNGPEFv2tbscyfZ8DKylnK/U/rIYF9pGUcwI9RyceIMzOLeXHte2parvfzlG5/wQERFRdNjNjbXtDb9+CkvmJgy/bRoEsf4G254eBFS2HoqTLZ8AAA7Wcn25akaC4Oww7Cvux1BxF4aKu/CI9R638tbK8ro0N2pym4/EVdY3MbRNKkYCbj3Vnjp9+mI3gOqEZW9HoplREXBQvW7dOpx55pna/qOPPgoAuPnmmzF79mxkZ2fj8OHD2nlFUTB+/HgcOHAABoMBXbp0wdSpU3HXXXeFoPn11xj86/VcodjE67lqvnqqQ9VLSTFI9f6lFQzJ5eGNKa6Rtl29DrYco3N+iIiIKDrKUhxJqiyqEQPXPAIA2LzsdJw86opoNsuNWss9TkGZ/0tfjRGm4x84hnlfYHkFI8RtWq/0NNOHAIAdSns0FUrQXCiA3RqahLORVn1fWDW1Gllpp+LOTY+gc0pXDIRzSWFXrkuIFeXnIjdjN7r2OyUSzY2ogIPqUaNG+ewtmz17tm7/gQceOCF7pXtjv9dzv/88Dzc6pmlgdc/xGOahjChx+De5s5mSsE7pjgJzB4Qq/cfqHk9DLTiIYf1O1Y6ZyrPxi+kZ2FQTgPNC9EpERETU0MmmxphmuwI2GPCk8WsAgKUop5arIs81mvE0p/qkFomIgwUm2HFg22qU/vQUjOc8hx6Dz3IrW4gUbbsYCRgWlwHUmIpdhEZoiXxHmazdQP/TQvJzRJJSFQNKVVF1eaO2+FMZgnPNjuWDa+vySXm3K1IArMt4HYMvujOMLY28+jsOI8YVIhEAsNHYT3e8de5yfGmaAgA4ojbDsGueBgDsU1oBAKbZqp7iefhwVytI8ZJcihq8wtSBuNI6EdOTHwlZncOuHY/h93ykG5Ylqgr6iAfRo9YBT0REREROdmMi3pGvwAfyJbUXjiLVlIhx1mcwzvqMx6HpcZY87Iy7BRvMd+HvHz9DX8tG7PnxNY91uc7O/uDcJKScdLpbGREKmlSt6DNozaMh+Rn8sW7h/7D3pYHI3L+zznWlZS3Bb6ancGvBewCcIxurH1AIHuapl8N92mpK+sd1bkt9w6A6hA4lD9K2F1f1Px8SO+jKJFQc1bYrVZO23VFwHL/PsAAAILp8uI+rSbo6yuNahqbBFHOcX1rhJVYNC5dqSeJBRERE5KpJ7lpsMd+G+SbncrGyMTGKLfJMFQ34T+mD/5Q+umVFqxmMjvt0g6Agp8xxPzRWWuWxrifVWdp2o2Zt0emsW93KdBCi01s/eO3j6Crvw/Gv76tzXUZrEXqKGUiTHT9LUlkGLhb/RffyjQAA0cPw7wfh6EDMU5O1Y4daX1DnttQ3DKpD6GDj4ZhhH6s7ZvMxwv4YGmvbD9scb/R1iqMXWklshYqqoHuO/dwQt5RiVfXUCzHMUbWhavqBVHPsEhEREZEPkr0SSUIFBol78K/cG/uVlqiIc2SLzjqwE0X5ebXUEHmehn8bTM6s36eLm31efwUcy4itanEduvQdDlN8kluZFkJh3RpZR0a5rO6VKI77wuqlXVsUrMO7pvdxbtG3AIAjqmMp1km2G7VLbFXhpgQFf8iDAQAWIbZXefKEQXUICQLcns9sN/TETqWdx/J9hIPOa6uuVKr6IAVJQgUcQfUIabvuuoTK+jcvhfwnKyqUIBOApWb9g9Xme/F00UshbpVedfZ5SVChKuytJiIiIv8Y7M6lY2+1PYHR1rdQ1KQvjh7ejdafDUPKu12i2Don1VaB66XFGCf9pSWCdWUwGLXtPaYePuuqHtnX8cLHAABGyT3EsqkSlst9AAAlanzQ7Q7WphpTUoOhVgXViuC4T6wOrquX1Johj8VbtiuxSHGO3pW1oFqGBY7fqSh7X1o5VjGoDqEEWz7uqVrs/Hz8BwAYaVuJHmKGVqbS5uz5q35jAc7hEtVr2YmCgNSqeRc9hUO610nL3xCG1lO4qKqKnzZlYU9OCcosdpw04Vdc+tavQS2PNfPv7WghFCLeXhj6hrqQJOd7U2FQTURERH5SXbqY2gvH0AL5gCrj4G9vR69RHgi2Mrxs/BSvGD/xfN5lKuZQcZfvuqqC6upEw3FG96VxjYKMn5SRAIB1Sveg2hyM1YrjgcAhY6c611UdVFfnfhJqLKk1Tx6Nd+XLcURtrl0zXv0UgCPWaSk4ErWVie49+bGOKaZDaHD2V9p2I1R4LOO6RNG2Ac9jVNX226YPAACnS1sAAKKlWCuXIsTmWnYnsh3ZxWiVEofGCSbM+Gc/Xv3dkRwiDYXYG3cvUAps+HsWBo6+PKB6O1XNvS+3hXepK8El+7ws2yEZ+FVBRFQfpS+aC2tpHoZe9mC0m0JUxXmPssj8JADgh/yfIVpDMPw4hGrr3HBddtQuODobLKoBkl2GwaAPmqt7qsWqIFMSBXwvn4rLpRW6ckKX0bh5bxOUGpviTERGddtUwT3QD5iqr6v6wUN1T3UiynGDtLhq3O2FAIDWyAUA3Gp9Al+aXgEA7EkYWPe21DPsqQ6T93E1AGCIfb3uuCQKKK4a8pHYzvswDKEBDos4Uazcdxxj3lmO/i8uQlFxKa7/+1QcjBuHg3Hj0FN0jjowr5gaWL2fPI6njPMAAMPEumdw9EUyGJCrpuCo2gSKbA/raxERxZpVs5/Bqg/uiHYzAAD9/70HQzc9h4wQZPYlCgkPseqIFf+HsjaOXtrDSlqEG+SZa4+6p1V3RNGlg6EqiHzVfh0sco0fUFUhCVXHXALxn+URbnWeOaQ//lH6QWjZN7g2KwpWf/Ma9mxcpjvmyz6lNfYrLaGGIOyr7qnW6qrRUz1I3IOnjfO0+1UAMMJxH2mDQQvwbUq4U+5GHrufQqjS6hzaXVGVPj5VKdAXUp1PjBLivE/S95QwwaUKqse+WHUInxhfhxUGrHj/S1zoMtJgjulVbdvoMueoWnlpEfKO5+P3gzIK9q7F1n0H8dg996B/u8YYkfG/iLQfACRzEoZYPoAEBZsl96UQGpKC3Gzs/PU9dDn7DjRvU/ehUUTUsNmtFgw/+D4AIGv/I2jduVdU2yOrAiRBRX4l4DmDC1Fkqap7kNcSx7FTtgEADqkt0FZRIYY762ptaumpFkURC+WhUCCiHRxtt0FCqcWORmZnCKUoihauSi4j/eLMZu2m3aIasLHFFZArirDY9DiWZA4AMDLgJm/883MM2/4ysB3AgCJsXf4jWv91Pw6OeBkDz7vJ4zUGwY7O4lG0kTMDfr2arGI8stVUlBuq1uWu7qmu+kE/c7nPVRUFsmxHO+EYAMAOCWLVw4eKnN0ARte5PfUJe6pDaMfREm3b25CS7W2uxN9KfyyWB6BbW+d8A4vq+BCmK50BuM5RcCeLDTvIiZaM/HJcPWMlJv+yXTdMP1BJ27/EWdJGjJHWYltpI6/luovuX24Jb7RH+1n9ceffg/BUxt343DQV93zwE3JLIjtyQRIFfGZ8FSvMD6GsrKT2C2LY4U9vwogD76Psk/q9niYR1Q92u1XbtlRE//uxuodM8fEwniiSLMYUj8cXHbDiYstLeNF+E6xyPcjXUsutniCKuN/+MB6wPQiL6hj+3VM4jLLSYl052WVEnyg6e6on3HKFtv33Gd9i+L0fw5a3H13FLNxpWBhUk0ty9uv2V/35NVJRjPwVn3q9prpXORRfEbtaXYIRlun4vtWjVXU6h3/nZh3UlVUUBVv+/kbb/9k8QdueUvRU3RtTzzCoDqHBLkkMzhVWeyyjQsX9todwu+0JINEZVK9WegIAZtvPB6Bfp9qtDv7hDIvJM2ajZ8ZXSP/3N/yY7gx4Zbsdq2Y+iq3Lf/SrnqnGmdr2BdIan2VtVmewfPTwbo9lVpofQMbuTX69dqhIooAzpM1oJeRj39o/Ivrakba31JFlX3G5USYi8sZut2nbwSScDCXdsE8fD+OJIimr6UgtCHXVvHgLrpGW4k7pF1jKit0vjDB/7qbFqntuUXF87scZlsCWtVU7X1KUj6VvXo/p9ktwquUdiPHOtZjbtO+sbScX7wEADOraGgBQpCYE1ebsRMew8UOKI4bIVRyvVwTv64CnwvG7Nqo2r2X8JWtLuzp+L0Wp/fCQ9V58n3gtFv72i66s3W6B1XbiTGfl8O8Qcs0MOAC+swQC+g+zWrUnVg0N9zX8m0Jv5UcP4CPrHFQnZP/foT7AwLYAgPXTb8Twwl+BI58ApxUBABRFxW1vfYVuRf/hkvjNsEtmmM98EubfH4PrAOI+4kGfr2t8xfGluKrbY1CPrENLL+XkCCf3cB2RZbC5D1NvSDYo3XGFtAJ71TaoH4t8EFF9JovOm2HVr9vy8FEUBdX9YubSI7Dau8BkYHBN0SUrKv4nX4BmKMK1hqXa8RI1HvcbfoAkqMgrLwYaN4leI+F8KKaogtdeRlFQoULR5UiylhVq29s+fxznWBbhHAOQp6ZAFG/wWI9UdS9lMDpGm5oQXL6a6qWsJMERL1wl/VNVn+eA+dt1GbhKSgcAdLPVHpvU+vpVv7PqFcOsiW3wo3Iqhpia4MC+v/B/JmdZu80GoVFzD7UAh8S26FDn1tQvDKrDpAQJaARgk6EvOlmdyalab34fwFmo+XysOpiOF6p6y0TvGfqKkrqFuLUnNrvNihHZc3THEgTnk7Whhb+6XbNi41bMKr0HkABUd3D+emXQbRi+502f5xv99pBuXMmplrexwnvxOnN9qJPUsmHPM64QHDfI3jL2ExG5skNAjtoYLYRCQJFrLR9OimLXguo9mXm44LvfcPcZXfD0GN9r6hKFk11R8Yb9GqShQBdUj5VWatMVrJbo/81V4lJwu/UxSALwkZcyWww3I86oD1hla6W23SVvibb9qOE7SOLb+uvNA9GrciO6jxoHADCZq4NqG1RVDbgTTamKH9oKedjx3y/oKWYBAIZ6SWD71HfpuKpq1qgSgtEsFWs+x/emX5BxdDSAflr7FRWIb90byHOWzdi+Bn3+vs3jkIBoP5AMBz7ODJM/MRwAsFfsrDt+af6nOBh3PXaZbwbKj2vHT5McQ0leNMwCoA9qqudbVyuLbx2WNp+ojh5yH3bdef0UAEBh3lHd8dVfT8XuDUvxzHfr3a4Jp14uWcMBQFXD/2VUrjoS6ZnivA8pagiq15E/yWU9eSIib2RFxWjLm+hROQtlTaIbvCouQf13q/eiJY5j/j+R/ftEVFOnw/OxwXwn3jTO0B3vJzrnA+cd2hbpZrlRpTgsVgbhL3Ww1zKKh1BJrhrSfDznCNLgTEicLJRDVPUP2vo89RdsT2eiSVorAIDR7FgBSBJU3VQSf7UvWqttS0smadsthEKP5X80Oecx55XVfTUXoegIBop7UXp0HwAgrjIP54pr0dOyCWMrf8IieZCz8KLndZ1UALBC7u1ouxrdB5LhwKA6hFznj1RnwZPhucfZLNgguPRGv2C7GQDwmzIMACDGORdFnyFfHPK2klPJ13e6HRshbcfR4wXY9ucs3fFhO6ag+0+XQFaj99HZp7SCNQKDTGZJV+I9+6WwxTUN+2tF052iY658mhD9+V1EVP+V5R7Ctrjb8K5xetSXhXGd0v2Y4VusinsAa+PujV6DiABI9jKkCqU4XdqC9+2e72FPXnprhFvlrnpJLV+dxZ56VBWbo5e90Qf93c7VTGguiCLi4p1Ja41mZ7Jha2XgvfWS4sz/EifXniixr8s0RDkEYd+jxu8AQFsiNqVwKz42TcNNpZ+ie/lGnCOtx3T7JbjcMhE97Dvcrp8lO3JHSUEOf6/PGFSHWbqhr5bR243LMIzqILx6WIcgiDimNgYADBT0PalxljxQ6HSxep5jkvnOOei4y/MyVivjHghnk3zqImbjL/MTYX+dOYbL8ab9atji68d6kkRE9cGRBS8AAM6V1sMe5QzGsmjGEbUZAKC/uE93TpEbXk8QxQjFGTDtVdpE9KXzjmZg42tjsOnvb2stK9jKcJm4HBcJ/3kt4ymr/v7sPFgtlYgT3HuaXZfU8sRU1VMNAJVBBNUF5rba9gahT0DXniFuDvj1XLkmZjxZcIw6qB5ZK0BFStUywgvl4digdvdYR4baHA9a78er4h11akt9xKA6hMwuH66z4cj6PMq2HP3F/Z4vcAmqRbegGmheNZRjgLhXd1mL474zSlNgKuGeoRJwLGDfRjju8Vygtpw5q/ZCAUh2Wfs6XKozO0Y5uW3EzLOPinYTiCgG7LU01rZN+Z7nMUaKoqoeR06t/OgBFL3UETlH9nm4iii8VJclpm40LMJK2fNa7u/9tSfkGfQPzn0YA8r/Q79/btcdr6wow5JJ5+H9N5/XjtkLszHN9CGmGd7zWp+n4d+NM//B339877G84GP1HgAwGE340n4WZtnPw/ebjvos64kI58OyFeinbe9Qal+lPkmo2zz2ZXucnXqGqkRpqE6cptrQFI5kvrlVnYIFqn76YIaShpOEDGSqTbFU6V+nttRHDKrDJBm1Z2t2Hf493jAXAHCxuBIAILo85UsUKkHhM1c+O+yv0feMy7Xt1WlXYoPSNeyvWVet1Rx0FrKg2qKfTCScqnt5vpJHR7klFElWu4Jya8MbfkZAeWlRWOt3DQKkkuywvlZtFBWwe5hmNiJ7DpqgGAcWTI5Cq6i+Ki0uwIEXT8bKj8M72k5wuYcdKO7FCGm7x3LTFu3E0l3HQvraCRWeA9X0n9/HaHUV7it5B7+u3AgA2LXg1Vrr8xRU/yYPRWYdFmWRIGOwuAvGY1t9lpMVFbKif+ggqM7f7XV251KvM+0Xul2/c/WfwTfSg8L8XLdj1T3V7e2HtWNjTetxm/Qr/pCdc9Wn2a7AdbYJeM80HfPNk2CtqH3oeqxhUB0mH6qOBd8H2dO9lnF9mmUUHE+exKqsiFC9Dyk7QToOIyZOCv3wvXw1EQ9Y78cj1nvw72WrAACrm12OfVJn9LvlHYiSyeu1j1jvgRKBRGS1ec/2ApaYH4c5331OTENSvayF1cuIBWqYzn71D/R9/leUVdZ93U6qP1Z9eDfMr3fAnvTl4XsRl6BalaP8/qkoQBfRPbAvVB1zOI8kBDY8lBq2Q9PHopNyCCOy5tReuC78zIq/P+4GVB5OD+lLC17ukvOFxtr2BX+MQm7WQRQWFdZaX82g+nzLVMgnXYBKe/D3aZ3FbPQVD+LkVGeAnFVYgT05zkBTVVVc+O5ynDPtH11gnWRxPoQYJO7Rtm0ecu30+O2qoNvoSWrBFrdj1bGM1nMN4EHxazxn/AIz5QvwqzwUAJCPJAhwltkRdyts1oa1hjWX1AqhFXJvnCo5shkWVi3C3kz1Pv9ZEL3/+sVaho9Q6CQpxfCSTy5opUIj/KyMBABM69cTADDsfucQ8LKkjkCx48ltrtoYaS5ZG/elnoaT8kbg7O6p+PDwRc5rVDMaCZH8Aqoe/h3dOYPhlgLH2pGXSisAMMHPiWKR9QaY4+zYtO9f9OvNwKOhGJ7zFSAAuxZ/hm79TwvLa5wpO+dfKlEOqmWX7MGZalNtylJjwdGNZhHNUWkX1S/b/v0FFQVZGGx1BkWHd6ejfff+4XlBNTyjgCyV5TDHJXg9X1ZSiJ42z1nFS+L0c7t//flbxPuRdHa10A9GeymsMOBUcSt+Nz+NzdlDkFdm0+4djyEVGUOfR6Pm7eHPegDx8QmABVCszpGAF723AvllVjwwuisGdWiCga3NuOP4q7CqRhwtGIg2TZMBAJLi+T6wtRD+fEvxtny3Y0KNZboOiu2QqDgeDqSiBL0ER0KzcjUOEvT3k3nZB9Gqw0lham3kMXILoc1qF7dj3p6Y/af00uYhAECx6viSOKQ4Fkn3NSfDLsZ7PecvVVVRZuHQRwC4QnL0aKQrXbDLEPiH2zXre7VKxYA1z56FbZPO83hNkxE3advHzv0A29EJizo+juP3bMOsu8/GxMv649Vxw3XXGJ85XLOasKqe39/QJ1VvUzsCAO4yLIxuQyiitD/uAp8tNyRrFMd3+D5T+Ja6iledN8KKbPVRMvwUxfE+VlTB4/2GqHAkxolOttvRe9H1GLxOn+A0c5Uji3NuiQXfrM1AhTV0ie1KDE1xUGnh9Xz15xQAGpd4ThZb08zpUyBNaYOVv8zC+kMFqLS5t/ePN2/Rtu+zPqhtWyrLYSnWB50VGZvQ2cMoj5peND6MO2yP4z7bw3jWdhsAQFIqMVpK18qYH1iFQRfcgh6Dz/LrZ+lrcQw/777hRe2YoSwHaSjErCWb8cNn07D3oxtxhbQC1xn+hsXlR62UkmpWBwB42jhPt7/psHsAXGcu01bn2qumzLnEK+uVbjhk6KitfHSjYRE6ijkoV814yDAfi03696C1og5j6OshBtUh1Fs4qG2fJaz1Wu5129W4wTZB90b8U3HMO5grOz6Qgo9F0dUQLN5+x5x16P3CHziQ17De0MGozs4+P/F6NL219myRrlZ1fxx7z5uD8scP45jaRDveXcxE86Q4NDJ7vmFv2bmvs+yQs9FrYjrO+b/n0LRFWzRNNOP6YR2QHGfEqmRnUG5yWYYhEtSqrwdVadg91XdaH3PuNPAHCOSgKopzqJoU4mEqFFUmOIJIuxjO6RzOv89qEOvMhlR1UA0Bx9Vkt9NNKjMj3SKqZ8rLPC8XOWL/e6isKMOQlxfjyfmb0WfiHyF7zeWm03GJ9SVcY3nO4/nXbNdo2/4++Lk9byoMgoIR6x7GUzO+wYR5/+rOK7KMy7FE228uONePznj9VNy050Fd+bsNP2OwqF9dxxNBsaKbcASAiko4pu4ZFP3DtJSm3h8g+JIMZ9LZNXH3YW3cvdgadzveMX2AgaX/aOf+3eZIeLxj9R+oPOhMVlwzEZirt3+rW6Zvj3Txh+N+yZLSBeNtt+Fe64O4wjoJ01OfgVzVaThWckx/tEOEWbDphogDgLWyYcUgDKpD6AzJ+QYeBu+L2jcRSrSJ/dWqs3dW956Ee/i3uGshrhCX4ecVG7RjJ+ryG4aqRQUvHdAazVp3wKquD/t97fBxz6H3yAuQkJiCkivn1X5BldQWbbFu4FSknzoDRpP34XlDHvwS6QkjsaqbI/DLVVMAAPYIrJNdvTaj6ufcqFikKCrsLl+D8gn6GTjRyC6ZaU0lDDoipSA3O+wP6VpW3UjHqf4n+DxWUomZy/ejoMy/Xue10snatqpEd8SXUjU9xyAo+EYehRus43Gr9XHtfFNLRrSaRvWEzeI92eiaeVORiHKkoQBxSu2rivjz+a0sL8WLuy/Gprg70cfL6jcWuOSVCeJh9mLzk+izS5+xOzf7oG7/bsPPqChzDEPuKgefBX+KZQoWmZ/EQ9L3SKlKQmxSKlGuOu7dVra5xdflHlWPSgWAcout1t9r4pJnsGnJPPT87WqMkZyddn+6JAIDgKMZzp9zVvYlniurw5QV1wTLWVVJXuVGLfGVfBZ+VRyjK+NNBig15lQmCxUeEyraGFSTPyrg+LBtlnq7nTtTTHfL5ldNrAqqawbdrooSvax7HYDHDd/gTdMMNK08hEM71gMTUyC+lHpCLr9hMjg+6EbJ8XEYfsMkrO75DGyq8wtgpx9LFRgMzp6RFbL7v3tNgy++B/3Pvs5nGclgRP8nf8Pw6x1LQAyxfIiOlXPR1fJFrfXXlaotqdVwe2+tsqINUwIAuz26QzkpMuwuvYsiOA0mErYsW4Am7/fAuneuDevrVAfVp5X5n/X2uo9XYfLCHRjw0iK/yu8Uu2nb0e6pVlweEB1TG+NscT1eNM7WjmWbOkShVVSf2KzeHzDlFxdja9ztWBt3H7bF3Qabj3XX5796G3ImdUbeUd8Pao4fPaRtP2f80mOZjoIzQ3ewdxiXSSt0+9s+1w8tbiEUYtPsR5CbdTDIV3AYJW0CADxinI83TTMAAHFKOX5KuAy/y0Ng6HVBwHXOSnKu0bxp8ulQaum8uEz6F/2W3eV2vBCNdPuyzTHf+tCudI/1dKn83GNw6y/X+dPWnpcBAMQa4UqCUYLsYVpVI7i/D+0VpUG3pT5iUB0m1evF7ZDc5+h2EbOxwXyn7tg1hqUAgMeNjuHHvuZUl8a39XrOH4V5R9FddPTOjN3xBDp87VxKqMXMgXWqu6EYds1TOHzlr9p+rpTmVmZKy7d1+4pLj0Uk1pEON62nWm24vbfWskJsiXOuZXl453qUFIVhHhLVK7KdgXSkHV3pGMkzpMi/Iaaqouh6b7ZmFmHD4QIfV9Tkf2be/NxsnC+uwRniJr/K92/rGDG0WumBPa0uqqV0eCkuv6Ph4g78n+FPtHVJWPRDpvuQcDqxyD4yLK8+pg+wfl7reTj0v9sO4IqK79BSKMDKRd/5fL2jee6f08OK/h7qPdN0n3X4o7rzqtpoy19uZYbnfou87Nrz0VQvremveFTAXpKLfDURthaB3zcPaeecFz1C2h70A31DjcRfdpujni0b13gqDhkirD4enNTKpaf65nOHAQAkWwmeNMzDZvNtWG2+F5eVfe3WUw04kyf+KQ9ytsfKnmry4rjq/JB0FbN8lk0V9E9n3rE71jH+zH5O6BtWw7EZY7XthhD8hUuXvsMx234uXrLdgBK4z1upmfXV7vKHK9kQ+zftf5lG4xP7GFjiW0a7KWFjr/EEv9uCi2Cb1i9KraFIcR3+bSkpjF5DTiAHTV0B6G+ovNmSUYiNE4dg/cRhUBUFdlnBRe+twOUf/Idif5dA8zHaq6aNcXdjhultfGaqfc1aAEiOc9wwHleTo74Un+rSI3SLwf2BxWvGjyLZHKqHKsxNvZ67W/pZt19wZBdu+nQNftnsuIfNKa7Er1uy0febkVqZvcW+ezpL/nrd7ZgoqHjWdqvH8nZDcMl3Wwr+PWSz2mr/zphmuzKg126MUtxg+AvjDH/DXuK+dnNtzrlMP2R80+dPB1wHAJws6keX2qpGzmRsXeblCqFOCelEo/NBRmqS475YOPQf7jX8hGShAi2EQiSbgA8T78EN1vG6a61Voz9n2i/AMtmRV8hW2bBiEAbVIaS4PBkvqlojcq00QJfl0JvqtdtUP56ux1ndv0gsleV+z1Xrbq89MQM5bO77LP5NuwZtz3IMu5Fd1o9OOq7v1ehy8inadl5ieJYIWGgaj4Nx47CxxkiHcPg27kq8ZL8R5Ukdw/5a0eJpWFwqPCd1oYbD9XOcvHxSFFty4jAIjkGesh+3HRNmfIWB4l4MFnejIDcTdrsNt0kLcav0G/KLvA8XPFpUiZn2MQCArCCHPWfsTvd4fOXMR7B56mjYbVZYDEnYp7RCjtoEdjm602NsjVpiqez9QWCawO+zE12l4D3JaQfxmG5/9vrjWLY7F/fP3YgjBeU4+61/MOnLxboOmJ2Zx32+nqc8LG2FPBzoeA0m267H67ardXlhysz+JfnytNKKzWqBqija3GlP+v1+uW6/XDXDouqHJ5fD/6Xn/pQH4T+5l7ZvkgL/DqiZeHZoZnDrhhvh+F0fVFqgY+VcFCc5Hl6Ok9x77QFgpvF12PIPeTznj5K0wbjL+jDulR9HfLzjZ7AV699DgikBu8x9sULpqzteJjhWOZIEBQ/b7sOAyhn4+PjJaEgYVIdQHJxPw04SjwAAzrUtwVCx9uUCqhOU1Vxk3pOWx1fq9jP374B5aiusf/vqQJrr3cQUbF/1e2jqigFzE67Hw9Z7UdK4p9u5t67pj98eOg0nn3oRDly9GEfv26ud6xinf8ImShK2n/811jS+AN1uCU/vQG/R8WXYRAj/PBRBm1Md9peKGrvVOeRqTtUokR1K+2g1hyLEbnDOQ+uoRHapuhPVqHzH1KbRYnqtZS8V/ta2N/02E6rdhueMX+J54+fanEFP5i/8BbcbfgMAiGpwQxzbzT3D4/ERRz7FyZXrsXnJ19jV5Ay8b78EBshonbvCY/lIkVXVrwcVdOKy2v3/LIwSN+Fg3DgcjBuHtLfboaKyEqvj7teVuUr6B+u37QImpqDkhZaOvDwuUpQij3VfX/w/TDB+iWukv1EkOEf/yX6upb5fdR81V1ZciPQ3L4byWle/6gCABMECs6AfTfiy8RO/r7/T9hiykart9+8eXJ6jf6UhQV2n57hB+0R2PEy02Bz/1osV55D0VeIAbftsaSNsJb4fivhiNzTCH8pQHGlxpnZMMOlHGgjGOC0BsOvDk6NCCxSoieglHEIzoQgFSEal0rCWtOQ3cQglCd4zLNbmNslxI3C9tLjWsjWTmGX8Pg0AMLjYvyQr/uj1+zW1F2ogNpgGY4FyKqwJnoc5V/++O/UagjbNnfNu2uGoW9lew8/H0Ie/QkqTwObn1EeN1SK0xHHAFvz7ur6zV92gF6qN8FfVHyHeoDZ8JcWFuv3sQ/6tk0rB6yTmAADMQu1DMdME5035mQffhuySr0Iq8t7LkmJxrjlrQPBDHH2teGA58B/uWDYSb5lm4EbDYjQt9r7SRySoDKqpFmqx+72KNy+5JLkzC3bsjbvJrczZ0kYM+nYoAMd9r2teHgCobD7A7RoAOKd4AQCgvZgLGY5g6kLLy8hr3Ndj+Zo8dSbsXDIHA8qWo5Hgf7b/Sg893jWnZPpilARc4ZIkLS4uuOVOU679H3YrbYK6tqbqhKsWu+O7q0WS40HFy7ZxUJt00pWtS8bt6iTLrnFI37Nv0I3IFY3xOLl8JdaY79UtofVE0qu4zfo4njN+gY+NbwEAOjXTJ1qLdfwmDrN+8la/ysVV3WjUfHrmibmZflibv+tWH8gtwbLnTvWrLNXOGhf7gbMvL5U8j1VxDyAlZ1W0mxI22et/AQDYIGlTMISgc5FSrGj+sX7I2crfwp9Nn/x3gbhat++61J3rfPiaJNUZsCcqwQ97tvno2RtxdK7+QB2WpwkFqWA/zpXW116QTljG/J26/WI1HqubXhbS1/jvN2eW7/39HsOZljfxis25usk/8skwudzf2gRHYLvQ/CySSg/49Rrfyae7HRu+fbLHstVzdj15xHavX6/nzSh1be2F/NCseSvcWGPecaD6Vy1Z9qzhCxyMG4fhc3vh6OE9WkxxVt8OsBn1yQrt1uDnMccV7cVD0nycVensxDPHJeBW4SVt1RvBGIe7iqejuVCou1YymrUVjjqKOdhsvh0XHpoadFvqIwbVIbRa6aFt56iNcTRjL4bKG/26dtMZM5GHxth8Ru1DUIQa865lyb+nZPe/NQenS1v8KgsA9rpkCIwhvW1bcba4HsbKvNoLA1g36DVsMQ9A9+teC3PLoqv6YY0a5DDK+i770C4M3+1IqJImFONhw3wAziH21HAl1ujVqBAa1tPyWKfUzC3iEkgrPtaS71LiDC73xAWXcDBXTYFNdc9tUr0mrRslukE17I73slUNfpkc8m3D4QJc/dFKbM30PKy5vqs5ZSJZqEB837FeSgdn5Op7UWpxfE7tsoIDaiv8o/TDDPtFmGK7DqdO/EdX3i4EnuDPV+4AAFijnIQ7rY8AAE6XtiBTdSRomy+fqvt8WLqPxcr27stTefNw43e1bVkV8D/TW4E02yuzvQQ/mJ8PSV2NBMe/cZxgQ94Xt0GSHftGczyOm/UrBlVmBz8yK6F4Px4xzsc5lfqkiGaDiISqNkjmRmgpuK+iYjBIEF06LZKFchjtXFKLvFindNe2VQjYMvdZ72Vb6dcn7nfmVWj6/AGcfGZgGQgB4FCKYxjOXqW1z3K3GxYGVO/WvfsDbkssuqXsE8w0vYnkfP8eOAweexf6jl+KJmmtwtyy6HIuqdUwe27zMvVPx6uf+ALAzjWBT6XY9Pe3OPhiX+zZ6C3rJtVXWw8Hnr2VAlNzSR1fag5n1vVU+1p6xuUB4Orkc/1vnAsFgse1eiV4ebioRHelh+qHDP7kYwm3Zbtz8dyCrai0NZxlGMstVjz34VwMPjwLn77/CiorYm8JoM0Hc9yO9T09tD3VADBtkSMJrr1qiHCemoIWQgHaCrmQJP378w/Tedp2WkG6X/VXth6Kb+xn4CdhNJ7oMB+/yMN15zcpXfCnMgS2qgC6jeCYO7zSNBKfy47vg2/tp2P6uAEwNtMPifalezPnA7V/2ut7uXPVFL/rqcloENHaQ/DpSaVqxFqlO/7qMbHWsn2smzCg0jHaxyaY3B642Xw8mKxV1RKrao3vm8+U8RgoOnIO5cP9d7JO6Y47S97HN+aXdMfjLMHP766Pov8t3IC4LmbfHIXYVpHqtWxB0/5ux3ytTe1LhakJfpWHYrnie17KaNG/XvNq/b+qfekTari0L00f8wtjmdXs/fNpTmwccH39/rkdHZXDaPrj9XVoFUVDb8G/4YcUvECWnipAkm7fdU613eojx4PLA0BZDC4BTguhENYaqwKoioJhlukYXPmh+wVRDqrr00iiqbO+wZ0bLsU7b78S7aaEzIrXrsRC8zN40vg13jLNgGWq/wmx6gvxgL6XeHXfiRBEEeWPO5M0boofWufXWbfdEVS3P7oIM42v4yHD97hM+hcXS/8BAOa7DN/+JdGZkdsg+zccuadtO35VhqHppa/AFtcEF0n6qWl3GH7FwbhxMAqOe5ZcNMFOQw9MfvxhGKqSaRWjERJMBhgaNfH75+pf4OyV7XfFU7rvp5o5jgJhMrmPMi1W9Um/soQWWNThEcQJNgwRdyNRDOz75mC+BZbm/fGs7VZUqCYAgF0Jvs3V+SbUGj93ChwPm660PI+0Xqe5XaecMR5t5CNux3tbNwfdlvqIQXUInS8651mIgu/FsQ63PM/H2cAUNeqMe20PY5L9Zp/lUnysSV39YaP6LU9Nrr1QiDiHf0fsJSNK9XEzrNj9H9J59PAerJk/Tdvnklyx5waD5+VHKDSKCvJ0a8r6Wv4xY+8Wt/VnVZeeY9niPaiW450PyuLt3pfYqekhq773SS129uwpsoz1E4chPe4uzDBNq3kpBJfvkVVfTkLmpO44eniP369dV9W/yzg/EsCF26/mZ9BOzMVTZW9Euykhc66sD0hThHIcL/Wegb4+Ginqk+mZUhxJWRMSU2B75hjU5wtQ3rLuQfV9Je9ixdypSMrfgrOljRgm7gDgvPccanCunmJy7bn288HQXWUfYbbpNSTlb8X6nbWPpMwYPgk9JqxGXHwjqNbSqrY4gj9753NQovq3PrbR5vwuSYwzolRwBtVHTg1+TrDR5D6l5BX79drc8QIxFa1f2I0B5znXtJbiA7sHlFM748pzT4dtwP/hB/kU5KuJSM8IfhpD9UM8VdD3fturwskeYga6N090u85gLYJNSgj6dWMFg+oQkgR99OEr4ZEU5IOit+2XoyJJv+RP69Jt+Nr0IqYaPvZ63a51S7yem2M/B0vP+snjuUjeHFDthlhnoGPlXHSsnFt74TrSHgupgfVU71z9J3KO7NP2LZXlWPnJ49i6/MdQNq/OVB8JjxQf52pK/uQUDN0yMQQtqn+O7N2K9Qtn+gyCiHyR7XakvNMFiS6rY1h99DbLc69zO2Y3N9a2D+d4Hy4oJDunQL1x8HKv5Wo6hiba/EvAsfZttYzMDAwWHT1w1f+ve82qOdWyomL4nrfQRs3B4e9DM0/SH/Wpp9qTdT99iP1bV9desB46nuPeswYAO7auh6WyHGt+eA/HMuv/KJdNahfdfs+RzvnURpMZgiii+/n36MoUwz3PxKbTvd9jAsC50nqcunsKRmR/DsC5tGw1+5WzsVfqgk2n/w/NUOg8EcR7+B75y1rLJLV0LnXVs4UjoKseopwUZ0AC/MsY3rvIOaXLaDTr7u37n+3+feUvQdKPphlQOQPz5NEY8+QX+KH5vbDc7FgVSDI6O73kpDaYL7v3BHsz9rzzkGAy4LUr++EF+y04x/I6PsrzL9u6R1X3AjW7DRupjgcnk42zYDa4h5aqbIdscAbVBarjwYRNlSA3oPxNDKpDaGX7u7XtctWMG/GL17KSGHhUvUY5CW/br0RlUkfd8QS5BMPEnegjHvR6bUah9yerNxkWIa1oMx6w3u92LntnbP4xbKhGdHbc+LVKCW4Jh0BUf2n+tjkTiuJfd/Xe9GXo8dtVaDHTuUbihnkvYkTG/9Dnr5tQmOf/0h7hZqrwPo/WV4ZhV0XlNi05R7VF8kAvpWNP2y9OwaC1j2Hjb/6v4RmrSosLai9EAVs//UZtu1SNwyz7eai0eb+Jau9hiKAsmjDDPhaL5QH4cqv3EVeTjw7FDqVdrW1a9cULwMQU7N3kWBZnpdIbt1sfd76ey7xtxUsi0OndZ+FUyzv4Jc2R8Ojfvc5El7kl/i/vU1elxw76PJ+lpvr9/V0Xrg9Sq61783IM3vA0On93LqyWyP1OQuXYzKs9HhcWPY+NX0zA0E0T0Px//SPbqCC0TnPcN6xpchGU5/IRl+Dek9i0hT6ZVfLELFhclp5a03ci+o2u21KrnXoPQ9fnNqDf6Ktxb9GbzhNBBNXjDH97Pbdx5PtY0+8ldOvvDD573TQNq3s8jaS7HIFqgsmAc6yv402bI4/RTh/fG65/40VJwiEh9Pl0Vis9UCKmYMYNg9AoKQWX3TsFLTv0cCsnNO3qsYd9SeMrPNabHO/sDb81dQvWx92DDXF3B/2g3Dn8Wx8+Nodzbrggiljd9JIa1+mD6s/N16Fj5Vx0s3yOUkvDmWLIoDqETC2dH4D8GnPCaup98LOQvW7zMkcmv+qgOq/UgvWH9DeIFYLvYReCZMKLz76Ar0cv1x3/fH0uPl/FbMj1xTvXDsD9Z3bFN3eNCPtr7ZQdvT7/VzYTf2zzLxg+vnOF27GEnA3adtaewOb1h5WXrL3L5T4o9pDzwJPbPluL3+UhumM1k6c0BAPXPo59Lw3AoZ0bai8co3au+i3aTah3Du3cgH1b6rak3tDCX7Xt523/h0n2m7Fkr/fhh6LgHgAqqoqp9utwu+0JdB/qferUzfnvoaeY4bM9Rfm5GL73bQBA1x8uBAA813QJfjM7l7ax21ySoXmZmy216IkjahpKRMff+kPZx7RzF8reR4aFWvIa9yHprkZZpiGz0Mc89BDJ2Zuu21dVFYNLnNMqPp/i7HRY891bWDvtKt2IgPqop83zGuSnyGthO+TscJADTPykyDLWzJ+GQzsitBRa1VBdObEVRMl7lviih/Zh50U/ABMdn89zrK/hP7kXnk+ciKFXOLJqrxuoH+68++LgRqApojNgr16zOlT6nXUdhl72oO5YUkoqhl07HmmtOwIA2jVthHNOPxW/xl2A262PYYLtFg81ORyFPsniBqtjtOhal+TEdZViErB78hic36el+7nU5thj6IY9hm5Ia9kGPcXDbmUS253sdmy89Khuf9RJzp9j1QunYMPuQ/rvOn9UD/+uJXwcet9sfGS/0HlAsUMxOEc/HCh1vg+X7Wk4iUIZVIeQ4NL7nK8mhXy126HiLgwRdkK01cg+WSPB2QWTv0WfT7vi+7ecPc/yv+/5rFs0GNGkkQnXnH4y5tpHa8evq5iL9T/NqHvjKSTSksx4/LyT0C41/HNT8qyOP3pthOPIP5bp30Ue1kzXfQ5M9Wfpon0HPA/bUyHA7udT3JuzJuF8Sb9m5QG14WSFX6OcpG13kffD9u3tUWxNaKyeN8XjcbupcWQbUs+VVVjQYd6Z6DL/PG0akGvvRkWZ9znLlspy7ElfDkWWdSM33jLNwFrzPVD2uz9880WtKMSD0vd41vAFWvnIlttHdH6m89DYY5ldnzn/LharCdj236+4rWymrkzFL09h3VtXOH5e2XPgFx/n6MG2Vg1dbL/4bo/lwu0P1fcD1kASxNVFbok+cP9jmz7j9G34AQBQkJuNoVsnYUjRn/j3x/9FpG3hcJq0Vdu2WQPrhV//43QM3TIRHb4eXXvhEBDUqpFXou9l11KaNEOPwc42jR01Ek8nvYIH73bmHBh44Z34Qx4MADimNkb3gaOQJbTwWueafpOx74o/3I67BtVlRv+ThlVbl3yO13O+Hhy4Gj+mJ3544mJsTTwFXQZ5r0+okfl/nOR4WDTEw3SQYCmCCNHLCFZRktBl/Gp0Gb8ajRPMiIMjEHZd5q/NgPPccu6MTtN/R5tUZwA9QtqOgXNPhmVyW7z13J26cpUVZZDtnkfrZTY/HZdaXsQ3qb6/7wRRxBS7M2mrKstQTc771mmmD/G44WvMM72Exln/eKoiJjGoDiHXLIAniwdwCN6XuAp2maJvzS8iJbfG002XJ+mVNhmLzE/ALNhxefHn2vFLFfclgmSXtThFg3PORsd+ziEzQ8TdGG8M//zdaJoffyWesd2G0pTQPXVsCKr/cAJAmyL/eihLEtq7HTsuNtO2VaN/iUEiQTqkX/pqmeyYZ3S6tAWZW/+t9frsQ7swVnLvxRst1aPe+DqyqvoehHZ29yfksWbYTmdPy1z7aG24cM1h/Ce6ab9t0raPzL0fv37wBIQXm2DT399i5ZcvIf71tlj/60yP12579yp0W3AR1sx7GcXQPwBME4rQMc57QP6S7Qa3Y7a8/XjU+B3uMPyKAVlfeb3WtZe6GQq1G8PMnFzkHnW8d4cW/a6VOai2QO8/3edE9rGkY3DxYvzw+u1Aoeee7w7W3XjaMBedD32LrSt+xhlSaLPYyoqKJ7/bhPf/3uuznF3y/Z26xnwv5Pzwz/vtte453f63P3znVib/WBZ+m/Oqtv9XuuNhjaWyHFv++R6V5fVrzVp/l0vavPJPr+dyiiu1JcYUWcY3709A0/QPQtK+akuWLsItz0zG4u05KLXYMWvFfhx1GZ0gVK/gUUtQXdMT5/XAsifPRLNEZ/AmShLaXfgEAKC5UAgAyOz3oKfLAQBDL3sAXfq6j95SXNaptsHLGvA+dLvlQ6xqfo3bdKsjAQ7NTo4z4r+nR+PVK917eqv9JwwAAOxQHPc3i2THyjhe164PwBu2qwAAR6S2PsuJkgRRkpBsFtGvavnPfxRnm81xCZg5fBHusj6sHTvnqP6hlWx1nzrTSLDgUelrVFY6HgwV5h1F3KutIU1u6jGngMXYGOlqV+SYO+iO32p8FRuVrto64TWVxrWEatD/LehvzsZwcQdyPUwdiVUMqkOpRja8JcIwr0WtYujmxAouT/xeXrhDl+XbVzIm18RqosH55TDyKv2QETscP5eqKNi5djGKCvLQkKw0n4K58lmwNGoT7abUK2dK6dp2vN2/bJGq6Hg4s0dx/i4PSM71IA0ehnZGS811Z39WnD0+V228qdbrj+Z7zvJ9reSY6+WrJ8+Voqh4Y/p7wMQUrP+1fsxdPrBtNTb++QXsNYblmetBhuFQKodZC8Sk/3wPoz3RlO90Dt1tUn4IFxxzJCnq98/tGLHHkd355NVPerx2YLmjJ3r4njdxheTeK634GHK4Te3odqzbgou07bjigygst6KkuADlpYVQZBlHMzwHnht/nYn57z6GNh92RdqMvlj9zWu68yeLvoPNyyvmo9N3noebp1Ycwt2GXzCkfDn6LHZ/EFBWXo6RT3+Gl569DyVF/q1F6+rXXxfgta2no92S+7Epo9Brub7NfOdnaS4UYuaSHQG/fiCKjue4jSD4RH7WrdzOryegeWG6tv+ScTYqykqw8aM70ffvW/DrlLrN2Q216fZLdfsrTKd4LGdb/Sm2Lv/R7X24bNli5L4xDG9OdIyOWDNvMq7OfQ+dxeBzi2Qey8Pyb6ahIDfbsb9/G0YvvRKzTK+j3bzRmP7Gc7hl8QC0fLsl5k5/Hla7oiXTE6TQrPLSa/j5WDfoNey9zDG1o3EH7wGpN9U91a/YrsPepqMCvj6lSRqG3/sxmqQ6hzRvih8G+ZrAO4G89RBXmytehHutD+JxmyN/QrzZ0fZQPIgVq8by+bvOvGRw/k3eonTGfqUldirtIJri8fDZ3TD4PGcOi2NCM921bQZ4nzqz6vt3AQC508/Vjh355Ea3ctU5xWouJfbMHdfjzfYf4LbbH9COVffoA4BFjMOuDtfqrulpd3wvmQ7rp53GMgbVIVTctJ+2bVdFwMeiWnYh+CdcNcMS1eXp46rV+h62gzne/5i79gjYLPoh5QcU53CeNoIj2+rqeS+jx8IrYH1nMKjhu9/gMlfKz7UYZcmE/UpLHFGbaclxFpudw8kk1f+s2v7KLKzA3zuPBTz6o+Yal76y9XvyxwbPmfFbCgX46Yt3EP96W6z++jV88PqzePrj77HmnesdCZIAvPPjCsya+R7sNisWpGfi8bwJAIBBax6tF5m2O317Lgb8dx9GSZvczikBzh+sz3LVxtp2b4v7z3oiG1zhDIa7iNkey1i8DC1er3TzWbcqew+qVyk9fV7bv/w/rJhyEZLe6oiENzpAfCkVLT8ZhC8+cl/aRi4+itOPf6PtD9v+ss+6A5GY6JhL7e3GesJrb+C/uAfxnPELJE3r5LGML2PX/R8A4GJpJT761fu89n1HPP/buNp4ILwJIjdu8zz3uKY+eb/h7JojeV7rjOEFPwMALpdWYHeO/mFkhcWOiyZ9jlcWbg9JW13V9l07yejMfbO653ikjrrPY7nOlVvR56+b0PKTQfjzJ2dW6tOXXIE+4kE8a5yL7at+x/A9b7ldW1FWirysQ6isKHM7V9O67buR9H5fnLZ9Ipq878jh02bOSO38SeIRPG1z9oKPy3sHz7/wBAYXO0YqCoa696xWGzz2LnTt53jI0LXfqR7LXGN5zuNxANoIy2eMXyGhsvb3MADMj78Cz9tuRlmKc61wY39HorE9Ulf0e+pPdOgR+kShnXsOwq/KcBQ36QUAaNModJ0DkuB4Dyo+F+H1rF2/URhtfQvnW1+FlNAEcUYJd5zRVZv6It6hXyqyTbf+XusatftlHMg4jG5w5lDqZ92AYzlZunIpRdtxp/QzBpT/pzvetXkSvrh9GIZ1dq6i8IrR0UmwRekIa+uhiJf0vzczHPeDI6XQf7ajhUF1CNkTmmvbmWozHyUB2cPc02C5/mFYZNb3HLz7xxbd/iz7eVUBP9A22RmMlyn6m6MK6HvSVUXB8N2O3ok0NKwsud1su3CKuAVGS+C9CScKcxP/evFLkzrjP6U3Dqkt8PNGx5dznMsaj3YvmXTr4pSpS3DL7LVYujv4ZBfjbbfp1pn3R6ty7/OpLt7rWFJn2I6XcW/ZdEzNugVDC37B8L1vY/lzp+ChjRfiliMTYHg5DSk/6Hu51nz7GlZ99Uq9GhFS7JJtdP3rF/koGVsukFZjuuT4/c+zj4puY+qZyz30MNc0Wz4f1324HC999RfKS4uw+uupyM0+jEGi76UYV+3JxrHsDGRO6o5Vs8frzn3czBEEr/ORBOgiyX1VinFZ7kF11z2fIE0I/brxy1vcBMnsyA/RX/DcSz4Nb+r2q4ef+6Nm8qAPsr334DYTilChmny+f18z+l4Kqa7sfo68Sxbch5/GC/qf9Yvf9PMr57x6D35R78cza0f4vXrE/q2rkXVgp+7YhoPH8PZHH6OsxDG6ARNTILzYBBvX/eexDnuNOaW9x9yFnsPHeCzr2kt/7oZ7kbl/h9vDx5JMz6MF4l9vg2Yfn4y4V1vjvQ/exuHj3rPbD/5miMffoS9TjS5TNKTwzK8XRPf72X/l3liten9ApromAPTzgfhy8+mYI5+nG1XY78yrsfviH9HiAe9D8OvqubG98OIlvfHd3Y4HGG2vehUHxfZY3ct9NEagBgh7UKwmYI3R+8jWmg5c9SfWD30TbQc454FLLuv0Jj65DUUP7kWzqqRsrnzNRe/0iftSW80/1P8bNitIxzPGrzC81H1KqTddhSxc0Kcl4g36Bwe5V/+IPDSG9dbIJXYMNwbVIVRzCMk56krdvutNgsFL8pNgFPfwvOwDAHQu1c+/roxrrg3nTjY6v8iszXrrysXXSMZ4cGeEslRGwd1lH+JL0xQkH2dPlTfVyyjUxqRacIPhL/yf4U9UHnIEqb2t6dr58qTAe2x82bnuLxyMG4fZxlexdff+oOu5XfoVo12GuwNAUYXvoc6pNv+ertfkmuAGAM6q0XMzbMcUDN/1KvZ94j0baaTdZXNOCRlS6fkmNBbU7JkywY7CSsf34LWGpQ2qFz4SzhbX46uci/DcrsuR8EZ7DNsxBWkf1b4GavuyLWj+UR+0UXMw/OAHWD7rWZSVFAIAmkiOuX327hcE1BZPWcObhiGgXiH3xo7eD8NgdswPNAj+jSxJm9EXByb2gNXq/r2iKCo2HymE1e6oK2OP+9+i9EWOYa3V79E1P7yH7S+fgt7CQcQLVhTCfZmkar1Fzyt4BJvbpSa74Lxh2KzU7Tt+/AH9995dinOkwaGZzgeQlRVl+OObGdixT/+z/bVmEzp/dy5af+YMUtb99CEGzu6Gh7OfQKM3O8DyojPDsvknzwmXKu36301Co2SPwaMnbeYMR2WFfn64/UDtWfQfOPYC2r/XCpiYgn+X6YPEzP3uQfmm7YEN6y9K8T0KJJREqJg4tpfX84dTnCMeuxz3vjyWP7oPHIXkxk1rLxikRLMBN43oiBbJjodHLdt1Rcfnt2DY1Z6nvwSivXAMyUI5SmtZocdVp97DMOiC22EuP4onDfNwr7QABpf4Iy4hESmpaR6vHfTwNzg87h/8JAe+gsyKOS+g0c75AGrP/l2TQRKRsu5d3bFOvYeh2cRDaNne98imWMKgOoTiLc6epbZCLpYJg3TnXYNqOURzWwDA3CgZiup56MiICv1chfiEJMRVzYtsJjmfePbsov9DWHN+hxjCnnWKDTsvnO/cUf0LNiSjs8fims23Y838aRhiXaMdk+u4XmrNoKjHL5cDAEZJm9ChND3oej0Nb73jxXdQWO59mGrzduH9QzCw1JFI7WjG3ogGezWX4wOAOUZ9L+CO1b9DVVWoqoqtK37GqtnP6JbHOVZSibs+X4ctR/ybi+8qFD30f82Zgm8mXIy84nKs/3UWVn14N2xWC4QX9VlmSxEHa6rze1l8KRXrFkYvI3FhXg5WzX3J/2z7UdajluWrvDlF1D9YOu3QdDR6swPWTrsGQvV3jSBhqdzPw9XRdaq0DeWHNmg91YHohGyYXmmGld/p5++/vHA7Ns+4FZ9OdORyMH7rPpex/7/3YOVH90F8KRWrvnoZQzdNQC/bVnQXHe+Vuw2/BNSW5buP4apn3sKcH34O+OeoKRnOe4nqJE6uilX/A4Z4wap9z9f8vu9X6RxNFPdqa5y3/SnEf3YOKq3OXuWEn12C5IkpwMQUDN7wtNtrVOslHsL7kx+A1aLv6Ki0O197bf8pWkbpDxs5M2H7SlS1+Sd9AHFK6e9eSnp2ypKrsPb5IdiaWQRVVdFmjnuyr6ZfBzZqKD+u9jXcg5VxveNe8zgcyd1GSNvRs4n3B057mp2tbcdb/Rsl2MW+F8OEHTBYCoNvaD0jV91ri352XLhKKN6Pew0/4UnjN35PrRNEEe2798fZT32NbWfNwavJ42u9RlVVbHhjLE7d/zb6qLurKwq4vR1G36ZtL5f7BHx9LGCkFEKJJc4MdpKguk1Ddf2jl5Mc2puFDNXzU6nB5foMx8NPd36RNR18KZYnno+FnZ5B68b6DKLfJuiHm+X/8kKIWkqxoseQs7HN5Oh1UmQ7igrysOG3WT6vSSvSz60bumUiusvO4ZE2Wf9HduW+41i523FTqKoq7pu9HI+9+bEuiJTtdqz86D4sem40hBeb4K/PPM+LvHjXUygv9S+I87ZcRJaaqm1fbfgHj3yd7rUOwcv6taH0yauPoOUngyC+lFp74RDIL7Piqg/dh/0aBf0f/J6/XYNdL/SFMKkx+iy+AcMPvo/db5ylnR/68l/Yum0r3vngHd2N8eapo4GJKcg6oO9hqawoR/qU0djzfC+kvNMFW6ecUWtbJ3y+GJuf749fX75Kd9xmteCs/VNxteEfNHurFQateRjDc77Chsmnu9UxRNyNC2usCzp47eO1vnZdqYqCn9+6G+/+73+63sLG07tj+O43kPpBL93vrajChvs+X41VOw/jyN6tsFo8L+ETSM/jyllPARNTcGTv1toLh9g6l6XaXA0p+h2DixcDAATJgO9lz3M1oy0+YzmMJves27sMnn+umkZsnYjv/+f4HrNWVuC59SNxg+Ev3G34GRv//AIZds9LDI3I/gIAMHzXax7P+7Lpb3027tPmdsN35hdx06Yb6pzHIaHSMSz7OFLQbvCFunN7Lv0Fj9ruCai+9MWOXvmcTPfRRznH85Gde1zb7yjm4MtfHL26uVkHMSKI+Zn32efANKU5PvpuISb+tA1ZWUeQ/rNzbnJqF+c83VKzI99MutIFdh+30NXT5epiiLgbv3/wGH5Mz/J4vq0Q2ANIufR47YWC1K7bybA/m4vDIyZrx5KKvU8Dce1Z9Xf49wOl7+Fr80tIPh7aTPvRVP1A/2R73UZKSlJg9yMJiSnofdol6H/e/9Vadv7PP2kP+aupAQTV1flqOvcZhi/sjvsENYg55LGAQXUI2RT9r9PnWybIhE0z7BehMlGfej/p+GZ0EI95vSYvyzk8KiWtLdJPnYFV3R5Fn1PG4rTHv8aFNz/lds2huB66/QFlDSc7H/nv56Rr8bD1Xhxv3Bcp73TBwNUPY8PrY72WN8C9ZzcryZkZNDHLOXS4qMKGfz99CiPm9sLWf3/FfytX4P2DF+HNkiew5vu3tXJfzngZI7K/wDmSYwrCWQdeQ8YxR2+qTdVn3E94w31JL0+OZXpewsEM59DMK6VlWLbL8xy+P7cdRfpB75+5ULmt4lNte8H64Ie3+yvveB72x7lnMvakZi9lb+sWrPrqFRw+motWOI5/4x7CTNObSF/iGL656qspOLnS8W/Y+jN9r8uBt85Gf8t6dKvqdetjSdcCR1VRYKl09ITll1nx0IwF+OHbzzF53xU4WTyAC2x/IvvQLihVD2yMrzSHJ8PEnR6PN2vjPly15pzWUJLtdggvNsHY4q/wYObj+D39IHJLLG5zbtPfcPZC3frie3h/37kYPq8v2n5xCkxTWkCpejCkqioWb81C/gttIExqjBkTbkD2xC5Yu2C61zaoioIRh2YAANp+cQqKyqzoN+lP3Djjb6iKArusoED1Ppy4rsZIfuQvEA141/R+UPW/ZvM+JQoAdhn1w2Cn9v4Fi6/ahQetzkRUa33M6R6kboXBrA+qj6jN0O5h/+cZXp75Giosdpim6h/qDPjvvqACw9p0Xupcn3vqe/r3RnFx3fKkqHZHL+8xYxtcfdnl2vEDVy9Gt/6nYUg3fU6O/7M+4bO+Af/dh6MTO6PlJ+693hdOW4Lpb0/WHbtts2NZtLSP69ZZcdfWcRi+9iG0/rg3zt41UTvuuiTUYakdPrBfjCXyAGxt4/t95s0P8mm1F6ryuPFbXPqj92HUgTAYAltSK+D6jSZdD6boYwmveDhHBgh+joRryIbZ1tReqCbV+TDM37W5azqrR3McUvR/MwvURKgvFOJIVW6oKze4r4bSqdj/HDSusVBB0/4A/M92Hmsa5k8VJeVW/RfDVYqP4T5K7UH1DVb9sIzVSg9MtY9DRXJn3XGjxf0P4mHR8UcsG82wf+UPzrKNUtH/7Osw/HrfPc+nDNZnUNykOF/zadvtWmZnati2JQzBAuUUlMY511wfWLbMa3khXb+cRZ6arGUeBYDKY87AcM6Uu/G48VsAQJ9F1+GUP51BxPBtL6KywhFI7cl2Hxq29ttXUVx43K0HFQAwMQWrp9+CQzvWY98Wz/PYsr71PBeqeh3Kal+Z9Ddvq799E3++cxcWzX0Tw4p+81hHuLz33eLwv0gds7MP3/Uq2s/oipVxzmU1tmTkAxNTMHyXfgj5mnevR1F+LlRFQU+be/bgDXNfgKWyHMKLTWCe2go/Lt+Aj7/4Eu8cvRmXbbtfV7bVrKE4NKkH/lsf2NP+g2J7dOnjniDmzy9e17a9fdftyC7Gxc/OwCvP3o0N+30nUNqzcRk2vXoOMvfvwP6t+lwb8779Cre/MgN7PtIP+R1Q/i/WzH8bmJiC+eZJbnX+PmkMVFXFh8/dhLO/64lUwTGH827Dz2iFPAxJfxZfvnwr7LLjocTa79/B4d3pAICSGkFUyutp2KRehc+PXoolL4zG9uxit8ze6UpnrZchEuoyEmREa/cbzO2Kc11V+cznsaqrcz3V+y8egbN7t8TbL72kHestHMJYi/Pzv0Vy5h1RJDMMqe2wWB6gHWsr5CEhMQUHBf+XZoyfEr55oACwquX12naS4Fi32G6z4unj+gRLm+f4DnJrI9sdD6FkoWpa28QiYGIROvUaAgDo36RCV/6iCy/FB/KlPutsCc+9qusMt+Fl46fuJyb6t6Z0bc6v5YHP/110Jt7BOCSc+wzMbWrPH+BJ44tfxuvNJtdeMAB/u0yV+Ec+GVvP+cKtzEVnjHQ7FmrFzZz3joKPoHrwUed682Jlw0p+G4xAVx8BgLgUzw+QA2GQRKQ8uAI7L/oeawe+itXNLkfxuIUQBMHnSIimCHxqFwD0aef4zks2NKzlOasxqA4hS4FzHlyRmgAR3odUtS+o/anU6edfVWsZb44NccwhylKawObS62Js1Niv668bqg8uFsvOL8rf5KGwy3UbLkYxInsLVpvvw9Gl/s0zHVCuX9KtWY1EQY03ObPQPiDqhyPWZJ3aGZiYgpeMs93OXZ77IZLf7ux+UZVhed+jw9ej0WX+eSj10AsTZ/V8w3aqVGP4urgLFrsjcC8uPI5h217EuQXz8LrxY/QTw99z7GqMGMST7ABJFaG/ubnpkOc5W0Pzf0HKu11h8TKUefihGdj4g3PuadM/70dKxl8eywJAJzEHI392H+Ltzaa4IUi+2/ODzyV7HTcMCzdl4ePnb8Dcafqgw2pXMOad5XhBmolnjF/ho5kfYvkeR/b58tIi3fDslbOeQrcfx6JfxRq0mTNct+YyAPyf9Dt+ND+PkWq6WzuGbvH+8PMCaQ1+fn4M7jX85LXM9bb5GP7sPJintsKQzc+j/dwzsODX33x+ds6SNsJ6/DBaCvr3Qr9JGzyuIe1LhRp87hBV0N+Qv9n8FXxl///27ju8qer/A/j7ZneXQielCwplU2ZLmTILMgTxy5ApIIoiIshSqKiAigqKyk9liAgiioggggtQ2aPsvSlLVicdSe7vj9CkIUmbpGnS8X49T58n995zbz6FZnzuOedz2uu3j3f97tFT9DxqGK8nvF8bjQ8jF+GEthp2Bg1EnZbdEPd0Em48sx9ZEy/DU6lL4CVSKf54mCjP1gzBsF5d8Z/ojeuiH1RPLNBfz/+pBVAoVKbLQwG4VW+0/nGyNgq/xxU+ZaYw2zX1sVxtuVpvURoNMR4mvvuv9dj1yUiTdq3v/oBT++yvwCs+/I6hkZivLh3ZzHhI+JMJ9TDktcUYkms6Sq60axLuh6NvdMGYttUBjfFNyE2SoqetAED1qGhMfH4sDni0wRlpdZuef6X6MbP7fQYa/s6WazqhXkIPdMkxvpHpoSz5KUtajyr6aVQSWSHVxgv8rTS//0tJh1X62dFPVaNhAnZFjcP+5qZLtdnCt0ogYpp2QLOeY9DihaUIr9WoWNfLl38TdqtouPnYpvezSE74DBHPFf79r6xiUu1AnlLDG+wtsVKhcwZkvsFFXm90G+M32xaSkwgTbkKiNv8ltKBLUl1S3ERyBgknDXNQlTIrF5iXCLgteuu3Cw7VOKQaDXVOhrnTqJz5X84aBAr30fL+euSIhg/BQ39+hweZ6Ug5fwy7vpyA/acvIWJK0YVyakpSsH9eL2zasrnItt7CgyLbWOPOddPKt5XyrB+6/eu/uiHLB5a8XETLkjVRvgYPchy/zndBgtZxqxJYS/WO5ffCgnNHW0mPoYuNy54Vpt7EX+EXoOtVLNiLCQC1hCsQtVpsWf0xxsg2YGDq5/qiR2cP/YvDF2/iomqgfumoWsJVDF68B2lp9+A+LwyKOYHY90Ff7Frxhn6YtSXtzawFbq2e0p1FttmnMp7P2ntP/yLP0W6ZYbJPEAS88pL1PZq7YyZDOeMGtK/ftalQVb4cRSWjNav7/W84LjWfge81bbCsyiuoE9dFX/Ph0S+VDR4zrgmyu8n7+GJ4S9SedRTxYz7T7w+qVgPunsY9nLnQJR4yQYO+cdFIH70XftOOI7peM/zVaw92Pn0WkVE1IZcKOPbI3w0ANEg0JK2zJM+jY9c++F2wvdIuAMj9a6DOU6b/F+bkPpwKc0wbjkl5ozE2dxxU7p76JTQBoMW2wWh1/yez59fa8ASObP/R7LGi3EnTfR/IUJv/zhNgZoqFp1KGD6dOMNO69JNLdf+m4c2Nbxa0nLDKpO27eabLoYVVdocgkaDxpJ8R/foBiDPuYV3COvyg7G1U18Ocfao4XJAY/93t9uuFxrWrY0eD2Vgj7Y6FM3Q3K6KjY8xdokRJBQHShx1KhQ3/1lq4AVNR2Tv2M27Im2jS7ZmiG9rhoKJ4a36/pn4GTbI/wzit4XUulcnQqNNA/WdveWNzUr19+3b06NEDISEhEAQB69atK7T92rVr0alTJ/j7+8Pb2xvx8fHYvLnoL9RlUUCMYWhNtCQFKUKg2XZ7fLshNtG+F8F25cvwuWW6RmdBN0VfeAaZ74mwNqkGgPsyQ/GzBo/0yqnV5WfoxnpVL7yZNwiZ3rbdMa4IHpfqhk83kpzHZxrDXOqG20ch+ZOnUXV5S8RdXYwmKxtgmmylpcsYaZKxFYk77JuLZo+7P5n2lIaIN422L2v9cUVrvtjfV7/qet8bpzp2HcztGuOhg9tafV3kOVs+tq3gT3kTJbFundqiHBGiISmwPE6l0caJxijZLxBmVcICxaePnooaP3bDna+M551PkH+Pc8pBOPBeL/2+pmm/I+5s8XoQXKV5pvESN7sidXONqwQEY099w1D0Beo+yC5ws+05jWFahUThAYlUColUCrfpF3FaVhM7Q0cYXXenXy+cfPxHo2vkW38/ErltDK/dsMrumNKzMfrOWo9hL+gSzbrT/oE4457Jl0qZ3LiHfGR36+ew5lfjlT5coiuqahCUD+dPt4+thfgauvcJQRCMlqraEqb74qhy98T2JgswTzkWn0/UzUXs8PovuDrmLDb7Fn1Do6A6g+ehaYMGRTcE8I+g+wJ8UQzEGk07/CHVfR/pn/ua1c9X/89hRtsn9/6O1Ds3zTcuwPOM7vXTNM+05z7fvsZzdNdMXKPfV9lLhX1eut6sO88dw08a88OTC5vfbkmytvoj21HY3Oo7fKnuhitaf/zSeSvOPWV55Eu+g9oaFo9VCTKM6NsZOhI+nqYV4XObF/2eLUgk6N2pPfpO/Qohb1zA3KD3jW6GFDRv2qsImmBcTLLFuOUAgJZ9xqLf6yvhptJVJX+rfwI+k+mmldyA+c83R3PL/g+Bwn0AhfdUi0LJ95qXBd887M3dYcM61c5Sb6LlKaw5YtH/f89L12G/6jn0Fa2vNVHW2ZxUZ2ZmomHDhli40HIRlIK2b9+OTp064ZdffsH+/fvRvn179OjRAwcPWn7zLauqRRsXydgsmK9cWmvox5DaWVTAGod6/4H2tU3vAp3TBkN4tCR5ISQwzFftIt1ndEyTV36S6m2q9lis6Y5sT+uKXFVU42Vrjbbjs7YabY+WbXRiNNZrkFl0b94STSK+1bQ3e2ytMgkatdrqnnNLy9ud0obihLwOzvXZBCSlos2b/2BXLV0SMlc7GPGtOxd57V4Z3yE14wEipmxExJSNyM4rXoEXjVbUXytu9h8Qi6iVcFUIKvR4WVH3td1Ga84Gh0bi9hjrq2A/+n4I6JKwdsXodS6tcqfeRNzQ2frt5n3H49KAbRgesAYfqp/ErSH/QC1KcFwbjokvvqjv9ZeoDAmGXKFEzdf2In6k8VJSNf83GzFNH4PqDdO5e7XkN9CybSJ2V+mLAy3m6/c/+hmW//94Sqsr4PloEvSzZz8obLiZ/LhUd9O6l2BbcU6Zr6HuRJsewzBx6mxU8VTqYwwN8keX8f8HcYb5KRa/+5pO9/KpVMXq5w/v+xb2+PVA4zFf4Nk2UVg4QJdk92wSYcNvYVgZ4dCf3yJmY1/4fFyzyOrg+dNmcmD5i3bTns8DSamIaWH8Ptf0lbVAUioqB4bizTzTpcQA4Bt1R7P7z2jN93atqfI8Gs06gLTx57EzZAguPPU7Gs06iC4du2DkW6tQbdZZdGsZi+p1muLBpKtY3t7yZ4SkiKrKu2Om4KiyEeo/pbt5cepx497+yY/bXjztuaFDMTrcdEh0riiDRCLAzdNbP/pjXO4LJu3y+borMGbaRzjZ7Xu4jSv6c9ARPDIu6B8rVJZHqBTsqT6pLbmlvko74WGvvqXvDa4kVyhxCqajcQCgbc6HZvcX9KpcN0VntnyxQ+MqzWxOqhMTE/HWW2+hT58+RTcGMH/+fLz66qto1qwZoqOjMXv2bERHR+Pnn4u/NmJpY03CmikqIcgct0a12Ti0Gijkpkm7ubV4C3O79lD943TRuNJpfmESotIuE6oi22zVNtTXQDimaIhzUuPhinu/fdPcaQBMvxBI3riP81pD8nlSWguzGv+L20O2ofb0najewNAbEzdgOu4+fxyTkz6CQll0nACw951EyKH74vv6OsuJ4I0r53BkTnuc2me+N2bP/AGQzvLFRdVA9JDsgF/6SazYebHQ58592vR9u172l1bFna8482xtscenq+FxgSWcLg/cZrZSapWg0vHFrrCh0slay3OhS4q5v8vwWo2w9PnOuDi3O8Kqx+DOqH2IeHU7VBkpqPOw91amNF89fLVMN+Lla1kfVA40rGTxb03jtYRbRXpDJlegxQtL0DhxeJFxJj9MpvPXSc6Zch3nn9yMHhNt+/vMFy7Y9nnpps20ql3BmzkFtX9hERZWfUe/osGueMO0gW8KFIg7KamBdYFjTdZIrl4/Ds3HrUBw1QhM7VYbHevoRsoN7GV5tYYvGq4x2bdn0bMAgJ1/GqbzCLMq4Zd9p7Hhk0lY991S7Nr5N4ZNewsffb3GKOH+GAMsPpc1Xu9tfrhpm449cLqnae2A4GmHsFDdy2R/jxG6QmzevpURP/pjfcE0c9w8vDCkbR28J5pWOAaALJX5EYf5WvSfinpTt8HTW7cMWq2mj+HWqGTsChqEa0N364eK5zukshxLPh83OZaMiMel/n8hRTQUszvd6Sv9440Bo7FF0wTtexf+2hAkEsQ07wQfP+f0VBe8CSHztFxIS64xrG3ugaKnNALAz6oemJvXHw98TKcTlFX5WYOmlM7G9RuzAUceW26y/5Nnu5ppTU7/X9RqtUhPT4efn3PWXXUuQ1KdK0rxqtb07swc9UBAbrq+pSX5d99tocpzTLGhZn3G6x97PdJLV56Gf0eoL6CxcBqyXPuqGZZnZ6SWh76VFT9pEnDkquH/1lyvS3XhGtpIdWtfZnhFwmOYcRGNuLPzLV7/nsK09zZ/mPLvjRYg5vU9mNGzHhJqmO918guoqv+ivSPqpcJ/GQAdpQdxRjUEqxWzsHm/YZmo5D++1c/73bN2AYIWN0b9nAOotaEP5n35ldE1Dv/1vVFxmI8VC/GLchpmpow2avef6IPDXdZgZt5QNMr+P1SLMP57EGfcw9G5/bBR07zIuAHdaJm/tI0sHhdn3MNn2iesuhaAQgsdNX95NXYFDcL+5vPRfNYeiDPuQT39P4TVtPz8O8Oft/q5S0pSnvkv9wDQO/dNbNGYLjMEAAtC3sWfbVbbNVS2uAJDq8Pd0xeSAsOupUrzNwf+99oKqF+/h8GvGRfwajlgilGiKLXx5vN3mraYkDsGf2h1RXGUKndE1Ysr4ixTu2qMBwBcSLBtnWHPAPM9Oub8FGhYumtGnc1AUiqkMhmeGToKHyXsxPFRlxHXxZCghsXqkurLWn9Evvo3ej83G8d9rBvSLpMrcExiun7274/vwJOtTKtXx9/+Htl5GsRKjNcY7rahGR7/73P0Pj4ecZsfxzLFexh3biR2fWGo9F9XZdu6yY/q0cz4b/cW/LDfLR5PtItDzcZt0SpnvtFxT5UcdQYYRlGMlbyO3Gn/QeVu+3Jw46fPw8GET7G3+QKj/VGDiu6Re1RA1UjEjfkUIZG6Oc27KxsSf//+1i8TFx7TGPvrG+bUF+zlHzD2DXSa9QeeaF7KPqMfzqO+rPWH3MNyRXZJgVnEbhLrvk/+ruyIRZqe5WpUoVLIQ5rojttC6cyJ/IPCUL+N8Y2r/c0/QJNI6yuPp8J0WkR55fSk+v3330dmZiaeesrynMqcnBykpaUZ/ZQFQoHF7NPM/BEd1UbgZ41tBUsihKLnM90NaYuIbMN8Vq90x1QlFiQSnJFFmz2mLUdJ9YsZC7BWmQTv2+VvSkJxpTcfDwC4qC38bn1pFibcwvaHlZkBYPfCYUbH00R3DJf+imaS07odggRB1az/oiJ6GYZ95g+P3uXfD1eFILRo39OmWAMbmd79vSn6mm3bQnISW5STkZWrxoPsXDT6+1n9seaHjYsbTbw6DkeOHcaNy2ewc+lkNNhmXU2HA9poNIjvjKfHvY2fJvWAXCrBrpoTAQDnn9yivxmwtcE8RGYbL+GyO2aKyfWmVl2KKXmj8GTODLxhZqinIJGgZuJYk/2PGpI7GQnZC/Dx65PwkjgRG2GcYOz2fxIAEDfmUzTpNlx/7Ufn2j4qpKVpUSFHWKtphQOFzM0s6H8jzS/3dve5Y7gwpzs6v/knDnoYTy3q4bYUL41+Fo891hXNZu1F2njrPgNeLGToqD3kBeZQyhSWR17IpKZfPQRBgHTqRf22X1XbkoX/9emHH8U2eP5/tr3mHhX39Bt4MOkqGnUaWGTbgp+P9VqZ9pha0nnIVOwMHoKT3X/ArKcMib+bQopXOtdCnarGyUjrvmOR/PRhyF85AuXDIbWaoEZWP1/UK8ajVfZqa6Jj07qo5G++SODZNxtbXNf9UfHXDa/7cBSv5oFEKtEvBbWr5kQEJF1Ak8m/6t9nvn31f9ik0fX0/lWpLwDgsbqhQFIqxBn38MmMiVAo7BsJI1coEdtpEGISDH8/xzqtRGBo8WutNB+7TPeaTEpFSITpDY7C9OgzBGd6b0DmK5dM3r9smc7nLPlzwaWCttAaPuluhs/Ns6LtnUflRSWkw1vIAsTSvUztgfhPcE0IwMnH11pdGG1vg1m4KgQhdWDFqe7u1KR61apVSEpKwurVqxEQYPkux5w5c+Dj46P/qVatdAzLK4ogCPpF1D+F6U2Dv7SNdD2+YuHzkwrSFlJB3JL8ZbNOKo3vQpsbPlX085uf+63Jc36VYHKBhx/aEZKib+6UtPOSCIvHLM1TBIC20sOIum9YrzrurqEo1X3RA95CFpoX+AKZ4lHPpriq952Jb9XtkCkq8aNct1xS3NgvUfX1E/Dyse3uc/UGLbGvmXEP2YVIy1/ug4R7qDNjMz59s+hiOPXXtEbQkqZFVqPONyp3Al7L0xWWig70Qnhl3Y3CuIGvA0mpiCqwvvN7/Rpi9/ROuD5iHw65tcDZJzaiWT/j5PDvtqvx3bPx2DK1J/7za4xvNMbzJLMmXgYAtG/RFBHZKwsdJn7KszmShiTCx02OBW+8ju5JG/Cp2vBluMVY++Zw2bOUyAl5nUKPXxP9MCHvefxV522j/Rf6bUGuKMN1wTAs87A2Ei2iKmNXFUNv/cluayDOuAe/wFD9l+jYSRtx4uG0g345M7DyJeMhvt6+lZE79Sb2NX4HAHBJG2CyvA4AzJ0+FdNClmCF9yjkTi3+a1xa8Eu/TGm5oQVKlTvO9d2Mk93W2Dwc/6lm1XA0qQt6NSp+VVk3Dy+r2gU8vwl7G8xC9uRrFod1W7p+/LMfI6aZ+bnC5jSqEY5gH8Mot1qdTJfGKuz5Ml/RDcs/4J6A2NcNSx9mv5qCo52+MWpfT3LR6msXZMu/gSX1EnoASam695lHhFZyR9TwL/FrzTfR6rnPjI454rkBwMvHD0ceW4ajHb9G3YTuRZ9gBUEigbevfWuSCxIJohu1hoeXr0NiKWk5WekAgKrCHSillr+/3vHWvW9miUo8nTfdYruCqmkuo75wHtLc9OIHWkrkD/sWClmCtzRo3OVphMw8g5imHYpu/FCzPi8hdOapQkeHlTdOS6pXr16NZ555Bt999x06diz8g2Tq1KlITU3V/1y5csVJURbf9xrdOqmxCtOYX5Stwz/Kl4Bc6+ZeAYCbYP3c5Q/ynsRe30TUidP1duW2n6U/ljf9Nmo2tm4NxYK0gmlSvVHTHHlS64ewU9klumDptPye0IJ2xC1C1IxDuPDU7ybHbsIPgkSC411XW7ymX/ZF/eNDBeal5s+jzoEuGVii7oojVWybK+RdKQBT1KNRN2cpflIZEjt7v+Q17T4Kb+YN0m/71m6HgyrLlUEvqgbiFblj13ysmf0VLvu3x4Q+1ldNDvBSITgsGg0nb0GNhq0gkUpxE7ovkgfjP0br9rp/1yAfFbZNao9Ts417FPOXNpJIBJx5O7HQUT27p3VEpzrGoyeef+trHIhbgOvDi7ee9+76SfrHu7S1caa35aXibo85gtrTLRcA2qhpjiG5UzC3T3280r8rNA+L0eSKMkTWbYGcCWcQ+NopHO/yLXZWG4n6SQcAAHEvLEPu1JvImngZMc07m/1bChq/DWuafYuvZ42Hl8q0yq5CqULTnmNwZ+ItrEnYgC8mDoV6+n/6IdZnem+Ah4cnZo/ui6cnzINCqcLRjitMrmOLgkl1TiXbeuTyVa8fh5jmRRftM8cZ6/AW5OPnj2Z9XoLKzfnDG5WevvrHv1QremkqDy9fICkVjV/9xajHU+XuiXoJj1s+0QaiE3pOa0VFoOvAcZArbL9pY636bZ5AvVbFG/FQUUke3NU/lsksF+SVanV1QdSQQq0VkZ5d9OjHiRnv42fla/C+faD4gZYSLSXHAQC1tGddHAk5glM+gVatWoURI0Zg1apV6N696Dt/SqUSSmXJvWGWJbsr90aLO+v028vVnVDbI8SojefdY/hM/iEeeFRDs/Hf6veH16yPjE1uSJV4o6rcvjUBH02qp+eNwCFtFEbv3Yqo6vZ9aaKyQ6I0fFnMEFXwFKwrKGKtC5JwRGoNy9L83fkXtG6ZACTpemuPa8Nxtdd36NxENzctsk4zXHj+MiI/NcypUo3TJVF14roiq95lqNVqnNiyBC2Ov1XgFzG81TUssDxc/ryuHMjhhQeQQw3pwy+Gu6v0RYvbP5jEfHv0YVw5+jdid+iGKSuUbng6Lgwrdl3GpC6OeU0s1nTHr5rmSKz6AK+16IJrVUKBr80vN1MSWksOY/HL1q2PW5jK008iK+cBYj1N59YJEgmeD/sJQy9Oxo2Atig4eFYulWBF0Ks4eT0M9SQX0EdqWELmsqQqLM2oa9x1WLFjbtH3ZVxp8Bju3L2LuBa6ivDnZFuw44ePUVdzAo0lhi8/BZfUKUiccQ+DvtiFS/eysf6FBFR+WAVa+sZ9/HftIir5697D80cy1IlPBOITja6hUKoKLV5Xya8y+nVPtHg8X2VPJSYW+LuUvaFbo93cxJ56rXoABe5bZYtyK8r8GcgLzIOWiiW7pnpF5+bmhjFhG5GdchiLhz1b9AlF+L/G6/HsgeIlkheVtRFb7EioLGvUZSgOHf4WWf4NUdhkR6mo6zDygK5ez4M8jdmbg+Wdu6Ab9dlUc9jFkZAj2JxUZ2Rk4OxZw5eKCxcuIDk5GX5+fggLC8PUqVORkpKC5ct11eJWrVqFIUOGYMGCBYiLi8ONG7o5N25ubvDxsVzEoKzK7zEK0P5nsY0t93Il1ZoBBZLqGerh+MrH+OuQIvs2EqV7ceaRAmU+fv5IHXcIPkXMIyzMVSEYdWF4se/U1sGfyonASeD88VhE1bGuQBGVTTVadAd26OZdHtJWR8LDpVMKc2nANoSvMoyK2FNvJsKa94DCJwgbv/0Mg68bhsFmtHsTyXkP0OjvZ/G9pg1a19MVYboyaDtS/ruHFvFtUeeR3o/IAMP7xu9+A9GxQFXT/N7OFk9NApIMSbXH+U3IfvAiVO88ckPq4U2CKoKuboOvkIGUh5W1o/rMAD43Tao9K1WBm49h+opUJsObvephfMea+iV0imvx0KbYcuwmJvaqCwAIqV7XIde1VqhQvIJD+WRyRaHzmD8d0Q7AbrPHvhjSFJuPhaJrk1Ac27sZdX/TDYNPaz/HIbEVplp0QxQcfFy9XgtUr9cCvyd1BqD7/DuiiEX+BJsD7glonKUbUvuHEIcOEglWPmv+Joh/SESJxe0IH2EAxmEVAEBjYfqPJbIC81kl2uIt90ZFWzSiFQDzS3faanSPNoCZDsC1Nd5Gn7PWDc9Nl/o6JBYquwSJBA0nby6yXdVM3XcJqSDiX+WLUN/dDHiZr+FTEWTIKrk6BHIAm8cn7tu3D7GxsYiN1d2PnDBhAmJjYzFjhq5X4/r167h8+bK+/f/93/9BrVZj7NixCA4O1v+89FLRVW7LshBt8Qp25BMv7yq6USF8Kgfql3qwhyTL+ObAedFQ2CT9xoVHm1M5U3BOsDUJ9Y1n9iO8ViP9sjYAENNxKILCouHn44XBz76KzMm3kCJWxkl5bdRNeByNOvTHqWFH0fjFlQj01vWLVYtuiLiW7SwWYsmefA2XB25Dx3GfmT0OAKdkMfrH9XKSTRJqc3pId6FtyucAdMnPb5VNi2mp3DxQo3E7HFY1w64g3TBtQRAcllADQIfagXjnyQZQFVgab1+Tdx12/aK8If+q6EYlLNBbhSHxEXBXyFA3oTtuP3sEJ7t9j3qtrS8I5Wi1nn7f8PiVX/WPQ/obKgb7uZft3pZxSYY594cE2yqJyx4up5MjyhDoZ3sFZnIdQRCwtNG3RvuG5U5Cn6dfwIWnfse/9d7ErZcu4eaIfdgZ8RzOPrHR5BqilCMMyToNEkfh5MN6FFWFO7h2dKtrA3KRI+2X4qiyEbwGLHF1KOQANvdUt2vXDmIhVeqWLVtmtL1161Zbn4IKUDQeBPxuWBu2MlIBtWOH4Bamk9Rw6zpVdMc2xcv67cDt0yC26+ewAiFUNtxAFdxsMxcNt5sWysmvmq2Abn7UkkovYcQjBVo83JTweMO4OnGtCNuKEqncPIosfuH+v8+Bb9rYdF0AgGD4e44Z9C5OzN+K2hJdjYT9zT9AE+h6YBtMMZ3fXZIadR0O7DdfGdoei9Q9cFWsgtdfn43MtHvw+7TwgluuViU4DFWCXbuUSrUa9YEk3fJsBfvfg8KisTN8DIKubELYEOsKwZVm36g7YJDsDyQLMbBl0oEgkeBkt++Rm5WGBn72FWYi1xnSsyuQbNh+c6JurnZknWaGtZ4r+SJw2MOidz8an19J5rzvJlS2qdw9ETN9p24ZSABN904Euo9ycVTOV79tH6BtH1eHQQ7CbKiUexDcHGmioSjYftVzqHTzkeGSJViJf5HaUFXWR8hCuOSWfjsIt/HrFstFfMqKX1WJmJfXDw+8Il0dSqmXI8oRlHQODR/rV2i7Sh663rom9W2rpO1I1aIbWt32nLbA0jIFkupqfu4YlDsdeaIUv2kaW72UREmQyRU43usXbBFaIr2QJZMm541C0+zPEJG9El93Nl/QZam6C+aqB+DNtz6EUuUOv4CquDFiX0mFXiHED38HkTMOo3Jg2V8e5oQYht80jXEZtlfSjmneCQ3a9S2BqKikSSUCrj79L3YFDcLtMUdQzc/8WuP5tsYZV9lP6Gl9RXIiovKGSXUJ8dPeLbqRFWRSAd7CA+sal0DhzYDw2oUeP/H3T0bbGrVxcZrDf32P5N9WojT7TdUFCzVP4IFXuKtDKfWUgnXrk9eZsAnXhu5Gw8csr0dfmiigxsGH6wgLgvHb4ubpffDLE0fQLukPc6c6VZ3YBHSeuQlevpVxsf9fJscfTLqKcZPexG34wE0uxdPxUUgZYlqd+oSiPg7N7Gw0vD4orOLOZyNjd2oPxqi8iQhvazr9gcq30Br1EDfmU4tF+Apq1/VJXBSDkCdKcXHUyXJxQ4mc66hSN5U0Fc6voE/kaM5df6IC8SikSrIgtX7OnUxS8ktUFCa0ijeQYvn4INnvOHXyGO7uXI7IjqMQtLgJLmkDIJ9wGLeTf0GDbbqevVt14hFQlT3B5dlubQzyF36SKZQIiYwptL0zLFd3whDZb7gl+iJAuG+xnY+QiWqCrn6AKDEuzuTvpXTI2reOFhHTGEhKxd1bKcjNeYCgajXgBqAqgItzDassBIUZ5sWmwR0YfxTv2rlmKlUMC/rH4rkbaagXUv6KiZJjhc88Aa1WiwgZv06S7TISP8Kz363FdWUk1hfR9ldlF2jSbiDekx0gVDrxXdDJXssbjily69d49ki3PMzTGaSywm8ABAr3Efjtw1l3i3VzCcMltzDs3fexTPGevt2NGyk4l+2NFpF+kLj4RsGjgtQpyBXuQpoXAyCgyPYVzUFJXcRqCy9SFpf9Mbq3agrLqym7hiKmM3acSoEaUgRI7xsdyxWlUAi6CsV5Baocy9JM15gvzfwCCk/4pTIZtK/fhVqdB+9ClmgiyqeQSdAg1NfVYVAZIEgkkLKuCtmpUlAkNmubAQ+ArFw13BWW05JNqm44djcNjbwjnBcgkQ34TuhE57TB+FVj2xJUMivW+rwb1Aq1spdhss88e0OzKNfPvnV3CybUANBvzX94+8uVWPHPSQDAil2XMGfTCWi1hgnholaLvfP7Y/e3Jb9kTkGT0t/Dr8op8Lm936nPW1ak1zBfbfne2JM46NEKlwZsw9Y3B+H1x0tfoStFnW74VNMLV0V/k2PLNF31j39UGn7HSumnnRKbM0mk0kLXPM73rf84J0RDREQEVPf3QKRwHWOk63Hpxh1Xh0NULOypdrAV6g6IlZxFXcklk2PrNS2hhQCIWquvV1RPMQBAIkUOFFALJbCUi4+hF0wjCpAK9lVFO6Uapnvw52tYIknGrA3HAQCeChle7KCby7lrySTE398E3N+Em0lfIKvfKkTWLW19nxVPw64jgNOzTfZX8g9GpUmmy6qUJulXjmCFwvxNGikMr8ME0XBD5bxnY9Qo8chKpzS3atCKAo6KEWjg6mCIiKhck0kl+Ev5CgDg8DdngamWa5cEam4gW7gHSV4mANMb5USuxp5qB7smVjabUJ/QhuFl+Q84oBoD5GVZf0Eb5l+XBIXSHZ+pe+AfTV30zH0bN0XfYl/zkw07cUY5GBdVA7H/j+/0++tcMRQ0C8QdRK7pXOznouLz8fPHf6I3AGCrxvqK2qWB9qrl0QfXGr6of5ypCsHo3JcxNHcyNld90eI55d3toNaIyvkGPXPfcnUoRERUgUjFnEKPT8uYgz+Uk+DzH0cVUunEpNpJaksu23WeqDGutvy9pg1y3Izn/XrcO4H35Z9iYObXdsdnSUxYEN5RD8DTedPRtm0HfKAufCkla0yVr4L84VzWZYp3gSQfZKTdg49gerPh0knzSwKRc22pplufPH/96bKiQUxNi8em9onHgRbzcUzREBEDPsRJ37bYpm2IphGVnBhh6TK2fQ20jq6C954sWzdPiIiobDrg2ebho9JVb4fIVhz+7WCvyr8rso0tbxuiVmO0PTFvDJZVMq6qrHxwC32l/+BMnuMHrXooZTiS1BlyqQQquRTT/9bFc1pbFTUlhZQFL8ST0u0m+zw/iDDb9v69O2CdR9dTuunWK1UIRc/xL02qRscCf5s/JpNK0DhxOJA4HACwZJgfjl9PR48GweZPqAB83OT4+hlOuSAiIueQanQ91HVzD7s4EqLiYU91KRdWM9bVIcBLJYdKrquOnAcp7omeOCxWd8pzpyavc8rzUOHCq4UhVXRHsFC2Colotcb1C3ZLdK+nB6LCpG2NAC/0bBhitHYzERERlZzsqC4AgBQh0MWREBUPk2on+EHT2u5zpY+s/eiGbAha1/UWNhbOQIk8HNY6Z81p5fV9TnkeKlzjlp1xPGwQUrt+4upQbBISYVy93qPDKzjaYTkyn+OcLCIiIldTBVZHhqjCOTWXNKWyjUm1E5zWhjrsWidUI1Dpxr8Ou56t5IIa7kIOlMjDQGEuAGC3NqaIs+zXQnISexYM1G9n5apx4PI9iKJ9VcjN+UPZAZ+qeyLbs5rDrlneSGUyxD8zD3XiE10dSrHIFG6o17oXqgSFuToUIiKiCk+Iaod6OUswJG+qQ7/bETkbk2onEFB+3iQ0om4YuAxavPX8YDxVZS2yB603arO25ju4OdK4wNi7eU/Z/ZzN723E8V2/4vjFa2g94zv0+XQHIqf+gsunk5Geeteqa6TeuWnx2Ea3HnhX3R9Z3s4Z0k7Otbtyb/3jsNrNXBcIERERGanu76l//CBPU0hLotKNSbWDvS6bgG/UHYz2jZf9YNxIYlt9uC/U3YoblsP0ku4AAIyUbUSUvye+e6ED2tYyngdTs1owAkONE9RX3vzc6ueok73EdN+v/0OdZbWxX/Ucekp2YKR0I8JWtoXXh5G4dCrZpL2o1WL/L4uR/NtK7Jk/AD4f10TWTA4tqogqJYwAABxyaw53Tx8XR0NERET53ORSNJacRhvJIWRkpFts95eyPT5Xd0e2p+NGfxI5Eqt/O9hJIQLDJKuM9qkEwzJEr+cNw1S5yqZrXhX9cUwbbnb9a2dTPvxdKgvGb3ynHv8BtTb0BQDUa93L6Fia6AZviYBjivqom3sEAHBU0Qiho1fDd6HxnFcAODb7CWDWCIsxfKRYaLQdvqotMl+5hCM/fYi4s/Nx0L0lcgJiEXfReP6vu2B+DcQqmv8QLtyFRG3D+uFUZtRs3Ba3gw6jXkBVV4dCREREBUgkAtYqkgAAV+/2BPzML2u5TtUbx+6m4SvvKCdGR2Q99lQ7mBxaVJdc12+fKjCf+orWH1s0TW2+5r7AfuieO8dicbB7gfFolP1/mOH9tu0B2+iEvA4A4KiykdH+Wk074mTiGtwebVgSYW5efwDAr5rmAACPJ+brj+XJ3OFbJQhISsUubW2jawkSic1VID3eD0fcWd31Y7N2mCTU+W7fuGKyb1r629imnADfW3tsek4qO6qEhJsU/SMiIqLS48rWxa4OgchuTKodLF5rPJdYBsP8kFWa9kiDO2BjIYYvhzbFs22jIJOYX+pHlCpwH17IlHiaPe5I/s98h13RE1B11GqTYzEtOqNKiGFVaTl0VcrV0M3DjqhtuKGQU2D4TvOknfrHxzqtBADcbj3LsYE/dPbPZSVyXSIiIiKyX/xV0+l/+Spp76Iq/oNEne3EiIisx6TawZ7UbDLazu+1Pq8Nwqvy73BCNQJQP7DpmsE+bpiaWBtyqev/u6oEVUPcoJmoVCWoyLYyQbdGcG6BWQaH2y7GPu9OqDNwrn6fRCIASanIm3YLdRO6AwDqtHrCwZHrZHtwLg4RERFRaWSpAnhS+hv4V/USfDiqkEop12dp5YwomP8njZLcKPa1k+WNsEHTArmqKkb7Pe6fxtuyxeiXZdp77Er169RDtihHZKShaFmD9k+i6YTv4e1b2aS9XKHUP5bJ5CUSk3jvcolcl4iIiIiKZ+Ts/0Pa/TuuDoPIZkyqHezR+2sXtKZzgwWYH8ZdlK88huGFvJeQ7lfXaL8y6xoGyf5AXO4Ou65bUtr0exGXe32PVkPfsvlcQSLB4c6Ov0nQ/uIHDr8mERERERXf4rzJSFuQ4OowiGzGpNrBtI/8k6odWGDd3mTcVWRyBWo2bmd3gagGLbviZPcfTPYf8GhT3NCIiIiIqBTY28i40G6oeN1CS5Sxb8JUkTCpdjDxkZf7fm00ftU0c1E0ZV9Ms45G25e1/oh95ScgKRVXRyQjWVvdwpkG2WLJDCUnIiIiouKpXIPfk6nsY1LtYHkS4zWo+8u24pAViZ81Prj/Ei6qBsLv2laHXK+sONN7AwDd2tbBrx2DINH92YaGRaL+DOOCFW+Io4y2c6ZchzLpPyRrdesa/qlpZHL9v5WtsVTdBdkeXMeYiIiIyJmkcmXRjYhKOSbVDiYRtUbbW9ACgslMa7JFdKPWQFIq6k3bZlTMDACkMhnOP7lFv/3a63OxM+I5AMC+ZvOgVLlDEATs0urW1z4nhphc/0e3vnhDPRRZPtEl+FsQERER0aMk3sbfzTQiB3lT2eO4Cb8EAJivGoP6adswUrYJxxT14dF+Jkb/2s+4kSB1TXDlVFS9FngQeRUqNw9IJRLED5sLYC6aFmhTRUgDAIQJt1wSIxERERGZkrp5GW33zH0bGx9p8488AbtyIhDjEey8wIhswJ5qBzsiqY231IMRkb0Sdaf9Ax/NHfgKmfrjM/OGQuAwF4dz8/DSDws3J3/udQ0hxeSYtzYV/rgHiSanxOIjIiIiIlNyqeH723pNPI6JESZtvnN7Cq+pn+GoQiq1mFQ7mMbCovUAcEv0xR/aWIc/5/2AOLTM/ghves90+LXLi06NawEAMpUBJseS0mZgr2osfG/udHZYRERERBWaAhosU3cGANQQrmG1YhbEQr5PE5VGHP7tYO1zt6OP4kds0zYE0B0Fi/9/oe6Gq6JpUldcWpkK11AFVSQ+Dr92eeHt6Q4AaJB3CJnp9+Hh5evagIiIiIgIMuRimExXH6eO5BIAIDc3FwqlYWSnpzYDlZAGQZPrkhiJisKeagcbk/c1Gkgu4EXZOt0OQZdUp4iVMV2+EhdVA4G8LIc816E/v8PpA1sdcq3yTpAa3piPbV7iwkiIiIiIKJ9coTLZl52dabQ9J2MaDqrGwPfmLmeFRWQT9lQ7mACt2f1VhTtGrexxXF4XV3I94a30Q8r5k2i4Xbd81L3ETZguWwHhQVUArey6dnl39eBmNHr4WBRYVZKIiIioNJDLFSb7crMyAB8/F0RDZB/2VDvYo8tnCYLj/om/9BiNEXmvIr1yA9y6n6bfr713EaNkv6BtzlaHPVd5owisqX+c4RnhukCIiIiISM9codnM1NsuiITIfkyqHexClXYAgCvCwzX3SqhXNOan7vrH/57lG09R4p58Wf/4njLUhZEQERERUWGyM1NdHQKRTZhUO1iD4R9hT72ZUI3aBADI9quNvzX1jBs5INF2EwyFGsKyTxX7euWdu8zwb/5gz9fY9eUEPMjMcGFERERERAQAxzqtxJ76b+i387L5HY3KFs6pdjA3Dy80f3KCflvm7otd2jpoLT1a7Gu/kzoJkcqzOH3tM6P9T2WsKPa1yzuZwlCobHDWV0AWsPM7OeKHv4PdijjsTw9FlFuQCyMkIiIiqpjqJuhGYJ48sQoheZeQl5vn4oiIbMOe6hKmlEtM5lnbSy7mwU3IxdmbaUb7z0mjHHL9imbj2RwAwGr3AZiiHo3MSjEujoiIiIio4prpPx8NchYjpXKcq0MhsgmT6hLmps3CINkfxjuLWbzswNkUo+37btWKdb2KIgWBRtvNJSddFAkRERERPcpNIQUA/JR8zWj/Hnkz/KBpjRz3QHOnEbkck+oSptKkI1i4q99+I28wBJnp0gG2eFs9z2hbqskp1vUqivXqFkbbPaU7gSQfKLJvwxNZELQcakRERETkKteuX8cZ5WBITv5stP9rt8F4Je85ZPnWclFkRIVjUl3CFEp3/eM00R1btY0c/hxnZdHokPMe3vWa6vBrlycKmE+af8gciqOqkah0Y4eTIyIiIiKifFtyh0AuaPB/ivnI02hdHQ6R1ZhUlzClm5v+8RJNV1wQgx3+HDfvpeKcWBW3pBwSU5gmbtddHQIRERERWaAVDau1ZOVq9I/lYi6UyAW0GnOnEbkck+oSplR56B+Pl63FceVwQJ1t38UeWYnrmDYcADBWtt7e8CqUnOpdXR0CEREREVkgEQzFfU9tWax//EH6RJxSDUOlmxxVSKUTk+oSJpUpjO66uQs5MMmOrXROVh27tLX126fFUP3jibLV6PrgF7vjrAgiWj2lfzw692UXRkJEREREjzquqKd/7H5slQsjIbINk+qSJgjIgdx4l51J9aeeL6J/7uu4KfoCAHLqD9Qfe0H2EzrmbLE7zIogMLQ6mmZ/hprZX2HYMy/ggLaGq0MiIiIioofCX9yof1wvNxlH/v7JhdEQWY9JtRO4CbkOvV5izlwMzZ2MhJZtHXrdimDx2G54r38ztKxeBbFJe10dDhERERE95OHlizuil367xu8jXRgNkfWYVDuBSY+oYF9PteZ+ChbJP8QB1Rh0k+xGtVCuT22rhtV80atRVQCAIJFgubqT4aAoWjiLiIiIiJxhQeNfMTNvKADgD22si6Mhsg6Taie4JlZxyHU2qkejq1TXu/o/2VYAumW68nlq7jvkeSqSfz066B9fOnvMhZEQERERkY+bHBmibvUcT9hZ3JfIyZhUO8Hj0l0ldu20wb/pHytExw4zrwiefyxG//hfTR0XRkJERERE3io5MqACAAQK97Di68Worjnv4qiICidzdQAVk33Dv80JrWGokqhkUm0zd6XhJRDfoHYhLYmIiIiopNW/uwWjFPMBALUll1H73AT9sVxVZRdFRVQ49lQ7wc7QEfrHb+Y9DUEmL6S17ebl9QMAXHXQMPOKxKtysP5xz7i6LoyEiIiIiJRKpdn9c/IGILMSRxVS6WRzUr19+3b06NEDISEhEAQB69atK7T99evXMXDgQNSqVQsSiQTjx4+3M9Syq/mw9/ChZBgm543Cdm0Dh1yzXc77+sdfabrg8Zy3MDZvnEOuXZEEVauBQ63/D2d6b4BEKnV1OEREREQVWs6Nk/rH/4k++sdT5atw6U6mK0IiKpLNSXVmZiYaNmyIhQsXWtU+JycH/v7+mD59Oho2bGhzgOWBVCbD0bCnsVrTHmfEUIdcc/rgHvrH6XDHUTEK58SqDrl2RdOwQ39EN2rt6jCIiIiIKrwqsd31j72QZXTMX7jv5GiIrGPznOrExEQkJiZa3T4iIgILFiwAACxZssTWpys33DQZ2K98VrehOQtIVcW6Xqc6gfrHvST/IFy4hV+0zYt1TSIiIiIiV6rRqA32XX4HTQ9MhkrIMzpWNfcSgEYuiYuoMCxU5iRhuWdRWUgHAOQ5sFAZALwqX42qwh001pwB8KxDr01ERERE5ExNe44BDkw22V81pqkLoiEqWqlMqnNycpCTk6PfTktLc2E0jtElbU2xr1Ejezm6SvYivVo7fFVgf1XhDgCgnfRQsZ+DiIiIiKi0+W/0IfgHcKojlU6lsvr3nDlz4OPjo/+pVq2aq0NyKHv7qdWQYYM2HnJ3X0eGQ0RERERUquwMH2O07R8S4ZpAiKxQKpPqqVOnIjU1Vf9z5coVV4fkWELxhn9LHDt6nIiIiIioVIkf/g52aLiEFpUNpXL4t1KptLhGXUV2UTUQALDlZi8Ay/X7b8EPAbjroqiIiIiIiBzPf8gyrF0+AYdC+uMNVwdDVAibk+qMjAycPXtWv33hwgUkJyfDz88PYWFhmDp1KlJSUrB8uSHpS05O1p/733//ITk5GQqFAnXqVJy7T6IDi5N1zvzJaFsDrq9MREREROVLdHQtRL+5EX1cHQhREWxOqvft24f27dvrtydMmAAAGDp0KJYtW4br16/j8uXLRufExsbqH+/fvx8rV65EeHg4Ll68aGfYZY+2BEfap0m8Eaz9r8SuT0RERERERObZnOm1a9cOoiia/CxbtgwAsGzZMmzdutXoHHPtK1JCDQDZEncAwFt5gyBI5XZdY7f/kwCAW6OSjfYvV+qGhR/SRtkfIBEREREREdmsVM6pLo92eHbCpvuhOKCtafc1WoxdDGAxAh7Zf1ZRG0/dfx2ZUGFjsaIkIiIiIiIiWzCpdpITbk3wh6ZklgbLlvvgkFi7RK5NREREREREljGpdhJ3TTr+UY7TbWg7AxL7hoCb81juVnyk+Ao/alsB6O6w6xIREREREVHhSuU61eVRSN4lhAq3ESrcBhxYCRwAOqauQbjkFsbL1jr0ukRERERERFQ4JtVO0i11VYldu65wscSuTURERERERJYxqXYBx/ZTA/u8Ozr4ikRERERERGQNJtWuIDg2ra733HLsrtIXR9ovdeh1iYiIiIiIqHAsVFYOqNw80OKFJa4Og4iIiIiIqMJhT7WTiA4f9E1ERERERESuxqTaSbT8pyYiIiIiIip3mOk5SZbEEwAwO28ABAn/2YmIiIiIiMoDzql2kr0ebfH3/crYo63t6lCIiIiIiIjIQZhUO8kR9xb4UxPp6jCIiIiIiIjIgZhUO4mnJg1bFJN0G2JXQJC6NiAiIiIiIiIqNibVThKgvoaakhQArARORERERERUXrBilpP0uP+1q0MgIiIiIiIiB2NS7QKCwJ5qIiIiIiKi8oBJNREREREREZGdmFS7AnuqiYiIiIiIygUm1U7C4mRERERERETlD5NqJ2FSTUREREREVP4wqXaSDKk3AGBuXn8XR0JERERERESOwnWqnSTZrSX23/fAPm1NV4dCREREREREDsKk2kkOeiTgT3W0q8MgIiIiIiIiB2JS7SQemjT8pHjt4VZ3l8ZCREREREREjsGk2kmqqG+ioeS8q8MgIiIiIiIiB2KhMifpfX+Zq0MgIiIiIiIiB2NSTURERERERGQnJtVEREREREREdmJSTURERERERGQnJtVEREREREREdmJS7SQiBFeHQERERERERA7GpNpJ0qW+AIB38/7n2kCIiIiIiIjIYbhOtZMccWuG4/elOCjWcHUoRERERERE5CBMqp1kn0db/Kmu7eowiIiIiIiIyIGYVDuJhyYV3yrehCgKALq7OhwiIiIiIiJyACbVTuKruYM4yQlXh0FEREREREQOxEJlTvLkvcWuDoGIiIiIiIgcjEk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUm1k4gQXB0CERERERERORiTaidJlVYCALyX95SLIyEiIiIiIiJH4TrVTnJK1Qi3UzNwTIxwdShERERERETkIOypdpKj19LQV/o3ooTrrg6FiIiIiIiIHIRJtZO8KFsHAJgh/9q1gRAREREREZHDMKl2kpNiNVeHQERERERERA5mc1K9fft29OjRAyEhIRAEAevWrSvynG3btqFJkyZQqVSIiorCokWL7Im1TOsh3eXqEIiIiIiIiMjBbE6qMzMz0bBhQyxcuNCq9hcuXEC3bt3QunVrHDx4ENOmTcO4cePwww8/2BwsERERERERUWlic/XvxMREJCYmWt1+0aJFCAsLw/z58wEAtWvXxr59+zBv3jz07dvX1qcnIiIiIiIiKjVKfE71zp070blzZ6N9Xbp0wb59+5CXl1fST09ERERERERUYkp8neobN24gMDDQaF9gYCDUajVu376N4OBgk3NycnKQk5Oj305LSyvpMImIiIiIiIhs5pTq34IgGG2Lomh2f745c+bAx8dH/1OtGitnExERERERUelT4kl1UFAQbty4YbTv1q1bkMlkqFy5stlzpk6ditTUVP3PlStXSjpMIiIiIiIiIpuV+PDv+Ph4/Pzzz0b7tmzZgqZNm0Iul5s9R6lUQqlUlnRoRERERERERMVic091RkYGkpOTkZycDEC3ZFZycjIuX74MQNfLPGTIEH37MWPG4NKlS5gwYQJOnDiBJUuWYPHixZg4caJjfgMiIiIiIiIiF7G5p3rfvn1o3769fnvChAkAgKFDh2LZsmW4fv26PsEGgMjISPzyyy94+eWX8cknnyAkJAQfffQRl9MiIiIiIiKiMk8Q86uGlWJpaWnw8fFBamoqvL29XR2OfZJ8CjxOdV0cREREREREVCRr81CnVP8mIiIiIiIiKo+YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUk1ERERERERkZ2YVBMRERERERHZiUm1k7wX8TkyRBUWqUa6OhQiIiIiIiJyEEEURdHVQRQlLS0NPj4+SE1Nhbe3t6vDsVtqRjZ8PFWuDoOIiIiIiIiKYG0eyp5qJ2JCTUREREREVL4wqSYiIiIiIiKyE5NqIiIiIiIiIjsxqSYiIiIiIiKyE5NqIiIiIiIiIjsxqSYiIiIiIiKyE5NqIiIiIiIiIjsxqSYiIiIiIiKyE5NqIiIiIiIiIjsxqSYiIiIiIiKyE5NqIiIiIiIiIjsxqSYiIiIiIiKyE5NqIiIiIiIiIjsxqSYiIiIiIiKyE5NqIiIiIiIiIjsxqSYiIiIiIiKyE5NqIiIiIiIiIjsxqSYiIiIiIiKyk8zVAVhDFEUAQFpamosjISIiIiIiooogP//Mz0ctKRNJdXp6OgCgWrVqLo6EiIiIiIiIKpL09HT4+PhYPC6IRaXdpYBWq8W1a9fg5eUFQRBcHU65lZaWhmrVquHKlSvw9vZ2dThEpQZfG0Tm8bVBZB5fG0TmlbXXhiiKSE9PR0hICCQSyzOny0RPtUQiQWhoqKvDqDC8vb3LxB85kbPxtUFkHl8bRObxtUFkXll6bRTWQ52PhcqIiIiIiIiI7MSkmoiIiIiIiMhOTKpJT6lUYubMmVAqla4OhahU4WuDyDy+NojM42uDyLzy+tooE4XKiIiIiIiIiEoj9lQTERERERER2YlJNREREREREZGdmFQTERERERER2YlJNREREREREZGdmFRXICkpKXj66adRuXJluLu7o1GjRti/f3+h52zbtg1NmjSBSqVCVFQUFi1a5KRoiZzH1tfG1q1bIQiCyc/JkyedGDVRyYqIiDD7dz527FiL5/AzgyoCW18b/MygikKtVuO1115DZGQk3NzcEBUVhVmzZkGr1RZ6Xnn47JC5OgByjnv37iEhIQHt27fHpk2bEBAQgHPnzsHX19fiORcuXEC3bt0watQorFixAv/++y+ef/55+Pv7o2/fvs4LnqgE2fPayHfq1Cl4e3vrt/39/UswUiLn2rt3LzQajX776NGj6NSpE/r162e2PT8zqKKw9bWRj58ZVN698847WLRoEb766ivUrVsX+/btw/Dhw+Hj44OXXnrJ7Dnl5bODS2pVEFOmTMG///6Lv//+2+pzJk+ejPXr1+PEiRP6fWPGjMGhQ4ewc+fOkgiTyOnseW1s3boV7du3x71796xKvonKg/Hjx2PDhg04c+YMBEEwOc7PDKqoinpt8DODKorHH38cgYGBWLx4sX5f37594e7ujq+//trsOeXls4PDvyuI9evXo2nTpujXrx8CAgIQGxuLL774otBzdu7cic6dOxvt69KlC/bt24e8vLySDJfIaex5beSLjY1FcHAwOnTogL/++quEIyVyndzcXKxYsQIjRowwmzQA/Mygisma10Y+fmZQedeqVSv88ccfOH36NADg0KFD+Oeff9CtWzeL55SXzw4m1RXE+fPn8dlnnyE6OhqbN2/GmDFjMG7cOCxfvtziOTdu3EBgYKDRvsDAQKjVaty+fbukQyZyCnteG8HBwfj888/xww8/YO3atahVqxY6dOiA7du3OzFyIudZt24d7t+/j2HDhllsw88MqoiseW3wM4MqismTJ2PAgAGIiYmBXC5HbGwsxo8fjwEDBlg8p7x8dnBOdQWh1WrRtGlTzJ49G4DubumxY8fw2WefYciQIRbPe/Sua/5sgaLuxhKVFfa8NmrVqoVatWrpt+Pj43HlyhXMmzcPbdq0cUrcRM60ePFiJCYmIiQkpNB2/Mygisaa1wY/M6iiWL16NVasWIGVK1eibt26SE5Oxvjx4xESEoKhQ4daPK88fHawp7qCCA4ORp06dYz21a5dG5cvX7Z4TlBQEG7cuGG079atW5DJZKhcuXKJxEnkbPa8NsyJi4vDmTNnHBkaUalw6dIl/P777xg5cmSh7fiZQRWNta8Nc/iZQeXRpEmTMGXKFPTv3x/169fH4MGD8fLLL2POnDkWzykvnx1MqiuIhIQEnDp1ymjf6dOnER4ebvGc+Ph4/Pbbb0b7tmzZgqZNm0Iul5dInETOZs9rw5yDBw8iODjYkaERlQpLly5FQEAAunfvXmg7fmZQRWPta8McfmZQeZSVlQWJxDi9lEqlhS6pVW4+O0SqEPbs2SPKZDLx7bffFs+cOSN+8803oru7u7hixQp9mylTpoiDBw/Wb58/f150d3cXX375ZfH48ePi4sWLRblcLn7//feu+BWISoQ9r40PP/xQ/PHHH8XTp0+LR48eFadMmSICEH/44QdX/ApEJUaj0YhhYWHi5MmTTY7xM4MqMlteG/zMoIpi6NChYtWqVcUNGzaIFy5cENeuXStWqVJFfPXVV/VtyutnB5PqCuTnn38W69WrJyqVSjEmJkb8/PPPjY4PHTpUbNu2rdG+rVu3irGxsaJCoRAjIiLEzz77zIkREzmHra+Nd955R6xevbqoUqnESpUqia1atRI3btzo5KiJSt7mzZtFAOKpU6dMjvEzgyoyW14b/MygiiItLU186aWXxLCwMFGlUolRUVHi9OnTxZycHH2b8vrZwXWqiYiIiIiIiOzEOdVEREREREREdmJSTURERERERGQnJtVEREREREREdmJSTURERERERGQnJtVEREREREREdmJSTURERERERGQnJtVEREREREREdmJSTUREREREZKft27ejR48eCAkJgSAIWLdunc3XEEUR8+bNQ82aNaFUKlGtWjXMnj3bqTENGzYMgiCY/NStW1ffZtmyZWbbZGdn69vMmTMHzZo1g5eXFwICAtC7d2+cOnXK5PdNSkpCSEgI3Nzc0K5dOxw7dsxsXKIoIjEx0a5/22PHjqFv376IiIiAIAiYP3++SRtr4i0Kk2oiIiIiIiI7ZWZmomHDhli4cKHd13jppZfw5ZdfYt68eTh58iR+/vlnNG/e3GL7pKQkDBs2zKExLViwANevX9f/XLlyBX5+fujXr59RO29vb6N2169fh0ql0h/ftm0bxo4di127duG3336DWq1G586dkZmZqW/z7rvv4oMPPsDChQuxd+9eBAUFoVOnTkhPTzeJa/78+RAEwerfo6CsrCxERUVh7ty5CAoKMtvGmniLJBIREREREVGxARB//PFHo305OTnipEmTxJCQENHd3V1s3ry5+Ndff+mPHz9+XJTJZOLJkyetfp6ZM2eKQ4cOtTsma/z444+iIAjixYsX9fuWLl0q+vj42HSdW7duiQDEbdu2iaIoilqtVgwKChLnzp2rb5OdnS36+PiIixYtMjo3OTlZDA0NFa9fv27297h69ar41FNPib6+vqKfn5/Ys2dP8cKFC2bjCA8PFz/88EOb47UGe6qJiIiIiIhKyPDhw/Hvv//i22+/xeHDh9GvXz907doVZ86cAQD8/PPPiIqKwoYNGxAZGYmIiAiMHDkSd+/edWncixcvRseOHREeHm60PyMjA+Hh4QgNDcXjjz+OgwcPFnqd1NRUAICfnx8A4MKFC7hx4wY6d+6sb6NUKtG2bVvs2LFDvy8rKwsDBgzAwoULzfYyZ2VloX379vD09MT27dvxzz//wNPTE127dkVubq7dv/ej8VqDSTUREREREVEJOHfuHFatWoU1a9agdevWqF69OiZOnIhWrVph6dKlAIDz58/j0qVLWLNmDZYvX45ly5Zh//79ePLJJ10W9/Xr17Fp0yaMHDnSaH9MTAyWLVuG9evXY9WqVVCpVEhISNDfIHiUKIqYMGECWrVqhXr16gEAbty4AQAIDAw0ahsYGKg/BgAvv/wyWrZsiV69epm99rfffguJRIIvv/wS9evXR+3atbF06VJcvnwZW7dutev3NhevNWR2PRsREREREREV6sCBAxBFETVr1jTan5OTg8qVKwMAtFotcnJysHz5cn27xYsXo0mTJjh16hRq1aqFv//+G4mJifrzc3NzIYoivv/+e/2+adOmYdq0aQ6Je9myZfD19UXv3r2N9sfFxSEuLk6/nZCQgMaNG+Pjjz/GRx99ZHKdF154AYcPH8Y///xjcuzRedKiKOr3rV+/Hn/++WehveD79+/H2bNn4eXlZbQ/Ozsb586dK/J3NKeweAvDpJqIiIiIiKgEaLVaSKVS7N+/H1Kp1OiYp6cnACA4OBgymcwo8a5duzYA4PLly6hVqxaaNm2K5ORk/fGPPvoIKSkpeOedd/T7bBmuXBhRFLFkyRIMHjwYCoWi0LYSiQTNmjUz21P94osvYv369di+fTtCQ0P1+/OHct+4cQPBwcH6/bdu3dL3Xv/55584d+4cfH19ja7Zt29ftG7dGlu3boVWq0WTJk3wzTffmDy3v7+/1b9vUfFag0k1ERERERFRCYiNjYVGo8GtW7fQunVrs20SEhKgVqtx7tw5VK9eHQBw+vRpANDPZ3Zzc0ONGjX05/j5+SEtLc1on6Ns27YNZ8+exTPPPFNkW1EUkZycjPr16xvte/HFF/Hjjz9i69atiIyMNDonMjISQUFB+O233xAbGwtA1/O+bds2/U2CKVOmmAw9r1+/Pj788EP06NEDANC4cWOsXr0aAQEB8Pb2tvv3LSpeazCpJiIiIiIislNGRgbOnj2r375w4QKSk5Ph5+eHmjVrYtCgQRgyZAjef/99xMbG4vbt2/jzzz9Rv359dOvWDR07dkTjxo0xYsQIzJ8/H1qtFmPHjkWnTp1Mho07IqawsDAAwNSpU5GSkoLly5cbnbt48WK0aNHC7JziN954A3FxcYiOjkZaWho++ugjJCcn45NPPtG3GTt2LFauXImffvoJXl5e+nnSPj4+cHNzgyAIGD9+PGbPno3o6GhER0dj9uzZcHd3x8CBAwHoerPNFScLCwvTJ72DBg3Ce++9h169emHWrFkIDQ3F5cuXsXbtWkyaNAmhoaHIzc3F8ePHAegS95SUFCQnJ8PT01N/Q6KoeK1idZ1wIiIiIiIiMvLXX3+JAEx+8pe8ys3NFWfMmCFGRESIcrlcDAoKEp944gnx8OHD+mukpKSIffr0ET09PcXAwEBx2LBh4p07dyw+Z1FLahUVkyiK4tChQ8W2bdsanXf//n3Rzc1N/Pzzz81ed/z48WJYWJioUChEf39/sXPnzuKOHTuM2ph7XgDi0qVL9W20Wq04c+ZMMSgoSFQqlWKbNm3EI0eOWPx98q/76JJa169fF4cMGSJWqVJFVCqVYlRUlDhq1CgxNTVVFEVRvHDhgtlYCv7e1sRbFOHhhYiIiIiIiIjIRlxSi4iIiIiIiMhOTKqJiIiIiIiI7MSkmoiIiIiIiMhOTKqJiIiIiIiI7MSkmoiIiIiIiMhOTKqJiIiIiIiI7MSkmoiIiIiIiMhOTKqJiIiIiIiI7MSkmoiIiIiIiMhOTKqJiIiIiIiI7MSkmoiIiIiIiMhOTKqJiIiIiIiI7PT/oZ100asgv/UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(df.iloc[:, 0], df.iloc[:, 1], label='Original PPG')\n",
    "# plt.show()\n",
    "plt.plot(aligned_df.iloc[:,0], aligned_df.iloc[:, 1], label='Aligned PPG', linestyle='--')\n",
    "\n",
    "# plt.plot(imu_df1.iloc[:, 0], imu_df1.iloc[:, 1], label='IMU Data', linestyle=':')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
